WP/09/03
UNESCAP WORKING PAPER FROM HUMAN DEVELOPMENT TO HUMAN SECURITY: A PROTOTYPE HUMAN SECURITY INDEX David A. Hastings
ESCAP is the regional development arm of the United Nations and serves as the main economic and social development centre for the United Nations in Asia and the Pacific. Its mandate is to foster cooperation between its 53 members and 9 associate members. ESCAP provides the strategic link between global and country-level programmes and issues. It supports Governments of the region in consolidating regional positions and advocates regional approaches to meeting the region’s unique socio-economic challenges in a globalizing world. The ESCAP office is located in Bangkok, Thailand. Please visit our website at www.unescap.org for further information.
The shaded areas of the map represent ESCAP members and associate members.
From Human Development to Human Security: A Prototype Human Security Index David A. Hastings
Series Editor: Amarakoon Bandara Economic Affairs Officer Macroeconomic Policy and Development Division Economic and Social Commission for Asia and the Pacific United Nations Building, Rajadamnern Nok Avenue Bangkok 10200, Thailand Email: amarakoon@un.org
WP/09/03 UNESCAP Working Paper Macroeconomic Policy and Development Division From Human Development to Human Security: A Prototype Human Security Index Prepared by David A. Hastings∗ Authorized for distribution by Aynul Hasan October 2009 Abstract The views expressed in this Working Paper are those of the author(s) and should not necessarily be considered as reflecting the views or carrying the endorsement of the United Nations. Working Papers describe research in progress by the author(s) and are published to elicit comments and to further debate. This publication has been issued without formal editing.
Since 1990, the Human Development Index has revolutionized discussions about human development. However, it suffers from two deficiencies, which can now be mitigated: geographic incompleteness and insufficiently “on-target” representation of economy, knowledge, and “a long and healthy life” at the level of the individual. This report summarizes attempts to rectify those deficiencies. In addition, steady advances in attempts to characterize different aspects of the human condition have resulted in indicators, covering varying numbers of countries, on a wide variety of subjects. If one were challenged to create an index on the condition of people-centric 1
Human Security , such as the authors of the Human Development Index faced in 1990 and expanded qualitatively in 1994, one could now begin to do so – at least for the sake of discussion and resultant improvements. A prototype Human Security Index is presented and initially assessed here. This paper extends a paper (Hastings 2008) with additional data, and is designed to complement the Hastings (2009) on geographically extending the Human Development Index. Initial findings are consistent with those of some sustainability and governance indicators – that stereotypical material development needs to be harmonized by good governance aimed at peacefulness, fair circumstances to all people, long-term environmental sustainability. The data show that most countries are characterized (in the draft indices) by one or more relative strengths, and also one or more weaknesses, which might help them to focus on areas for improvements. Indeed, no country ranks above 0.800 (on a 0-1.000 scale as in the Human Development Index) in all components. ∗
United Nations ESCAP, United Nations Building, Rajadamnern Nok Avenue, Bangkok 10200, Thailand. 1 Human Security is currently being used to describe a peoples' sense of inclusion, of being valued, of being safe from perniciousness (by other individuals, organized crime elements, or from corrupted governmental or corporate impositions), basic comfort (as opposed to “luxury”) and freedom.
Another initial result of this work is a form of documentation that GDP-“developed� economies are not necessarily highly developed societies, in terms of equitability, social fabric, or human security. These societal characteristics are arguably more important to contentment-happiness-satisfaction than raw GDP per capita. Where the Human Development Reports of United Nations Development Programme pushed the envelope significantly from GDP per capita to include health and education, the equitability and social fabric documentation now beginning can push the envelope even farther. Thanks to the work of many organizations, we may now begin to further characterize human security and societal development, and perhaps rectify challenges faced by societies in such dimensions of life.
Keywords: Asia-Pacific, Asia, Pacific, Development, Human Development, Human Security, Index Author’s E-Mail Address: roi@earthling.net
Acknowledgements I have spent most of my professional life researching, developing, and documenting indicators, most published perhaps in the environmental sciences. Parts of the observational process have often been enriched via proxies – such as using satellite imagery and geographic information systems to help characterize desertification. I have also pursued a sideline interest since ~1972 in socio-economic characterization – and since ~1987 on an indicator based on per capita income, literacy, and life expectancy (e.g. a flavour of human development index before the appearance of the first Human Development Report). Three factors made possible these ESCAP Working Papers on HDI (WP/09/02) and HSI (WP/09/03): 1. Many sources of data have become available in the last several years. One can thus compile data to geographically extend the HDI from various sources noted in Working Paper WP/09/02, and also thus find and assess additional data sources for possible applicability in the HSI. 2. Many individuals and organizations are now crafting indicators on socio- economic- environmentalsituations. When carefully analyzed and culled, several of these can be assembled to begin formulating a Human Security Index. Many have previously said that such an index was impossible to create. However, it appears that we may now begin to do so. And we may need such an index to better understand possible paths that countries may take toward stronger economies and more equitable-welcoming-humanely just societies, for all their peoples. 3. Discussions with United Nations staff members and other friends/colleagues have enriched my own ideas. These also built upon the profound awarenesses (that even monetarily poor people from advanced societies may instinctfully have – whereas people from less advanced societies may need great help in becoming able to perceive) on what a good, highly developed society may be. Somewhat as the Human Development Index allowed us to look beyond statistical GDP per capita, 1. The equitability-enhanced HDI presented here may begin to better characterize how much “the person on the street” may benefit from an economic engine, or from claimed delivery of education and health-care. 2. The Human Security Index may better help us to understand the different situations and challenges of nations/states – as societies in a world that in many places reports on economic indicators of interest to corporate or government executives – but may have not so systematic use of social indicators focusing on “freedom from fear”, “freedom from want” or “life, liberty, and the pursuit of happiness.” This may become more so as source indicator efforts, and approaches to HSI formulation, evolve and improve. In this vein I am indebted to numerous colleagues for their insights, direct or indirect encouragement to better formulate these ideas. Though far too numerous to list them all, I specifically thank Mr. Osama Rajkhan, Ms. Atsuko Okuda, Mr. Cihat Basocak (who also co-designed/produced the maps), Mr. Pak Sum Low, Mr. Ravi Pereira, Mr. Htin Aung, Mr. Jean-Michel Sadoul, Mr. Clovis Freire, Mr. Jorge Martinez-Navarrete, Mr. J. T. Denny, Mr. Wu Guoxiang, Mr. Sivasankaran Thampi, Mr. Daewon Choi, Mr. Le-Huu Ti, Ms. Hitomi Rankin, Ms. Aneta Nikolova, Mr. Marin Yari, Mr. Christopher Kuonqui, United Nations Staff Representatives in ESCAP and around the world, and participants in the Development Seminar. Prof. Venkatesh Raghavan of Osaka City University and Prof. Mamoru Shibayama of the Center for Southeast Asia Studies at Kyoto University and others from their institutions encouraged the release of the Human Security Index at the GIS IDEAS (GeoInformatics for Spatial-Infrastructure Development in Earth and Allied Sciences) 2008 meeting in Hanoi, and also have been invaluable advisors and confidants on possible avenues for encouraging balanced discussion on Human Security. Mr. Aynul Hasan and Mr. Amarakoon Bandara encouraged me to present my ideas at an ESCAP Development Seminar and to submit the manuscripts of these two Working Papers. Ms. Kiatkanid Pongpanich and Ms. Srunya Nopsuwanwong co-edited and prepared the manuscript for publication. Many people in Ghana and Thailand, who each hosted me in 6-plus-year work assignments in their countries, and several others in my visits and stays in ~100 other countries/societies, helped me to gradually perceive what I might never have been able to see had I stayed in my own country. It takes such diverse excellence to “help one to get a better wrap on things.”
CONTENTS
1 INTRODUCTION …………………………………………………………………………………
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1.1 Background – the Human Development Index ……………………… 1.2 Human Security as a concept …………………………………………………. 1.3 Extending the Human Development Index- an Earth Observation approach ……………………………………………………………..
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2 AN ENHANCED (INCLUSIVE) HUMAN DEVELOPMENT INDEX …………
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2.1 Selection of input parameters ……………………………………………… 2.2 Computation of an Equitability/Inclusiveness Index and an Enhanced HDI …………………………………………………………… 2.3 Discussion of the Equitability/Inclusiveness Index and the Enhanced HDI ………………………………………………………………………… 2.4 Discussion ……………………………………………………………………………….
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3 A PROTOTYPE HUMAN SECURITY INDEX …………………………………………
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3.1 Constructing a Human Security Index ………………………………….. 3.2 Discussion of the Social Fabric Index and the Human Security Index ……………………………………………………………………….
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4 ADDITIONAL DISCUSSION ……………………………………………………………….
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4.1 General discussion …………………………………………………………………. 4.2 Discussion on each constituent indicator ………………………………
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5 CONCLUSION …………………………………………………………………………………….
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6 REFERENCES …………………………………………………………………………………….
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APPENDIX …………………………………………………………………………………………….
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1. INTRODUCTION 1.1 Background – The Human Development Index Traditionally, for want of something better, socioeconomic development of economies3 was assessed by using some indicator of income per capita. When the United Nations Development Programme released its first Human Development Report (HDR), (UNDP 1990), it captured the attention of many specialists and countries, for its now widely publicized effort at a more robust composite indicator. The Human Development Index (HDI) contained in the HDR focused on three presumed aspects of human development: health, represented by estimated life expectancy at birth; knowledge, represented by adult literacy rate4; and economic standard of living, represented by gross domestic product per capita (GDP) at purchasing power parity (PPP). Such data were also compiled retrospectively over several decades, with recent HDRs estimating progress at five year intervals since 1975. The annual release of the global HDR triggers various news reports5 and analyses in developing and developed countries alike. At a minimum, then, it continues to focus attention on socioeconomic development in a manner that covers more countries than The Economist magazine’s Quality of Life Index (Economist Intelligence Unit 2005), or many other formulations which mostly followed on the heels of the HDR. Naturally, the HDI has attracted some criticism. One school of thought argues that the HDI is too simplistic, not adequately representative of the profound concept of human, or socioeconomic, development. As a result, several indices have been formulated, such as the aforementioned Quality of Life index, the World Economic Forum’s Global Competitiveness Index (World Economic Forum 2003 and 2007), the World Database of Happiness (Kalmijn and Veenhoven 2005), the Happy Planet Index (Marks et al. 2006), and the Wellbeing Index (Prescott-Allen 2001). 1.2 Human Security as a concept The first published major discussion of this concept was contained in the 1994 HDR (UNDP 1994), and extended by Commission on Human Security (2003) and others. Human security6 has been characterized as people-centric “safety from chronic threats such as hunger, disease and repression as well as protection from sudden and harmful disruptions in the patterns of daily life – whether in homes, in jobs, or in 3 Economies often mean nation states. However, they have also included subnational administrative units, cultures such as racial or ethnic groups in a nation, and also entities whose status might be subject to disagreement, such as the island of Taiwan, the separately administered northern portion of the island of Cyprus, the area formerly administered by Spain in the western Sahara, or “dependencies” (in their various administrative forms). They may also include supranational entities, such as the European Community. 4 Literacy was later blended with average years of schooling, and later with total educational enrollment, to form a composite knowledge subindex. 5 Some news reports have claimed that country “A” had slipped in its efforts, where other countries may merely have been more successful and overtaken country “A”, or that newly added countries with higher HDIs had pushed country “A” to a lower global ranking number (despite, possibly, a numerical increase in HDI for country “A”). 6 http://en.wikipedia.org/wiki/Human_security , Tadjibakhsh (2008).
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communities” (UNDP 1994), and postulated to include economic, food, health, environmental, political, community/social, and individual personal security from hostile actions by foreign or domestic antagonists, or by circumstances which can be managed by good governance (such as good response to environmental or cultural threats/hazards/disasters). Simply stated, human security encompasses both “freedom from fear” and “freedom from want” (UNDP 1994). Human and national security are considered to complement each other when they are in harmonic balance. Human security is considered as multidimensional. It addresses people's dignity and sense of self-worth as well as material and physical concerns. It concerns protection from self-centred attempts at hegemony (as opposed to peoplecentric services) by individual, institutional/corporate, or governmental elements. Some specialists consider that poverty and inequality are root impediments to human security. The 1994 HDR contained a draft “social world charter” (that the authors of the HDR hoped would be adopted by world leaders) in which it advocated for the United Nations to “become the principal custodian of our global human security” (UNDP 1994, p. 6). However, one could argue that human security watchdog functions should not be delegated to a “single point of possible failure” but should be watched over by a diversity of stakeholders. Thus, one might recommend instead that governments, civil society, individual advocates, and the United Nations might each watch, and hold accountable, everyone's actions or inactions toward enhanced human security – and the results of such action/inaction.
1.3 Extending the Human Development Index – an Earth Observation approach This paper extends the Human Development Index with indicators that attempt to characterize inclusive income, knowledge, and healthcare as actually delivered to people. This paper also goes farther, and drafts a prototype Human Security Index (HSI). It follows an initial presentation (Hastings 2008) of this prototype Human Security Index, while adding an additional data table and discussion on potential further indicator development. Hastings (2009) addresses two additional challenges faced by the HDI: extending coverage to many economies lacking a current UNDP HDI, and looking at the robustness of the UNDP’s own indicators that are computed into the HDI. The author has spent over three decades describing, through in situ and satellite/spatial data techniques, aspects of the Earth that are not yet directly detectable. This attempt to extend the HDI, and create a prototype SDI, is influenced by such background. Just as one uses multispectral satellite imagery and other spatial and tabular data to monitor drought, assess landslide risk, look for gold or reliable high-quality groundwater; this paper describes attempts to assemble proxy data to characterize inclusive human development and security. For two decades, the author has also been concerned about “cultural bias” in the development of indicators (Hastings 2002). How to ensure a minimum of cultural bias, and an opportunity for diverse cultural concerns to enrich concepts of human development, and human security? Such an effort should harmonize as many concerns 4
as possible about such human conditions that describe comfort, or true social (as opposed to militaristic) security of ordinary people in a society. What concepts are involved? What direct or proxy indicators might be developed and used? What indicators are available now? What improvements might be made in such indicators so that they move toward better value in describing human inclusiveness/comfort/[social]security across as much of the cultural and political spectrum as possible? How can such indicators best describe current conditions, and help indicator developers as well as governments and supportive institutions strategize improvements in the human condition of a place? This effort attempts to respect such concerns.
2. AN ENHANCED (INCLUSIVE) HUMAN DEVELOPMENT INDEX 2.1 Selection of input parameters The HDI attempts to characterize money in the pocket of an individual by Gross Domestic Product (GDP) per capita (adjusted for purchasing power parity to compensate for differing prices among world economies). But how much of GDP gets into the pockets of a typical person in a society? Perhaps the GINI coefficient7 of income inequality may be the best widely available indicator to combine with GDP per capita, to give us the beginnings of an indicator of “money in the average person's pocket.” The GINI coefficient is a decimal fraction between 0 and 1, with 0 indicating complete equality (e.g. everyone with the same income) and 1 indicating complete inequality (all income being received by one person). Some “free market” proponents have argued that income differences provide incentives for people to do better. Others recognize that extreme differences foster a lack of feeling of well-being, and even despair which has arguably lead to civil stress [including crime, terrorism, or insurrection], let alone a diminution of “people-centric” human security that many people would like to characterize and improve. Currently, the GINI coefficient ranges between about 0.20 and 0.70 worldwide. Values below about 0.30 or 0.35 are considered as relatively equitable; the highest values indicate great inequalities. If one had the opportunity, it might be worthwhile to investigate the computational “tails” of the income curve that could go into an enhanced GINI coefficient. Should the top and bottom quintiles remain the standard for calculation in an enhanced HDI? In most cases, the top end of the scale might benefit from a narrowing – such as the top 10 per cent, 5 per cent, or perhaps even less – to focus on the truly rich. This figure would vary by economy. Likewise, if one were to attempt to represent the relatively poor, the bottom-end tail might be larger than 20 per cent. It might be worth considering 30-70 per cent, for example, to cover people who could be considered most poorly off and/or vulnerable economically. Even in the USA, about 60 per cent of all households may be no better off then they (or their “identical” ancestors) were forty years ago8.
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http://en.wikipedia.org/wiki/Gini_coefficient That estimate is interpreted from http://en.wikipedia.org/wiki/File:United_States_Income_Distribution_1967-2003.svg, and accompanying article http://en.wikipedia.org/wiki/Income_inequality_in_the_United_States. In the latter article, Allan Greenspan is quoted (as of late December 2008) as saying that such high and 8
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The HDI attempts to characterize knowledge empowerment by basic literacy rate. This paper uses data from table 5.13 of the Executive Opinion Survey (EOS) of the Global Competitiveness Report (WEF 2002) addressing the equality of education available to rich or poor children in a country. Similarly, this paper uses data from table 5.14 of that document, addressing equality of health care in a country for the poor versus wealthier people. Note that global executives, working with the World Economic Forum, are concerned whether their employee or customer base received sufficiently egalitarian education and health care for an economy to be competitive. Both of these tables rank results on a 1-7 scale, with 7 being best. When combined with basic literacy, and life expectancy at birth, these indicators may get us closer to characterizing inclusive delivery of opportunity for “knowledge” and “a long and healthy life” for the diversity of people in a society. 2.2 Computation of an Equitability/Inclusiveness Index and an Enhanced HDI Table 1 (placed at the end of this report) shows (from left to right) economy name, GDP per capita, scaled income (using the HDI formula adapted as described by UNDP 2007, and Hastings 2008), literacy, scaled literacy, life expectancy, scaled life expectancy, Basic HDI (Hastings 2008), GINI coefficient blended from UNUWIDER (2008), UNDP (2007) and CIA (2008), scaled GINI coefficient (scaled as in a remote sensing linear contrast enhancement to a 0-1.000 range), educational access equitability (WEF 2002), scaled educational access equitability, health-care access equitability (WEF 2002), and scaled health-care equitability. In Table 1, where C3, C5, . . . C15 are, respectively, values from column 3, column 5 . . . through column 15 in table 1. Basic HDI = (C3 + C5 + C7)/3
(1)
Equitability/Inclusiveness Index = (C10 + C12 + C14)/3
(2)
Enhanced HDI = (C8 + C15 )/2
(3)
2.3 Discussion of the Equitability/Inclusiveness Index and the Enhanced HDI In virtually all cases the Equitability/Inclusiveness Index, and thus the Enhanced HDI, is lower than the Basic HDI. This suggests that delivery of economic, educational and health-care benefits may be at least somewhat less equitable than might be apparent from the HDI9 even for many high GDP economies (where many people may still be poor10). Many analysts suspect that certain parts of economic resources entering an economy are lost to inefficiencies (possibly overly high topmost executive compensation, possible inefficiency or corruption, etc.) before they may benefit the middle class – let alone the relatively poor in any given economy. If such economic resources went to equitable basic infrastructure such as educational and health-care benefits, transport, connectivity, etc., perhaps this could be justified (by growing income inequality cannot really be accepted – but must be addressed (e.g. Solved). 9 More analysis of weighting within coefficients is also needed before blindly accepting this preliminary hypothesis. 10 Poor – in the sense of being at least somewhat deprived of egalitarian opportunity, or equal access to basic services.
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someone). This finding suggests that societies could benefit significantly from improving equitability of basic infrastructure deliveries. Indeed, some traditional HDI leaders may not be leaders in Enhanced HDI, because of lower performance in equitability. Economies in Asia-Pacific tend to be about average performers in table 1. However, the mean HDI, Inclusiveness Index, and Enhanced HDI for Asia-Pacific all exceed comparable parameter values for Latin American or African economies, in initial assessment. The Asia-Pacific's average Inclusiveness Index value is about midway between that for Europe and those for Africa and Latin America. Asia-Pacific's average GINI coefficient of 0.395, and its average egalitarianism of education access of 3.6 are both better than those figures for, say, the USA. The Asia-Pacific's average inclusiveness of health-care access, at about 3.5, is almost the same as for the USA. Of course, these are averages for the given samples of economies, which might change if more economies are added to the compendium. Moreover, table 1 would benefit from being more complete – if indicator data can be found for more economies (particularly with respect to equitability of access to quality education and health care). Columns 9 to 15 of table 1 give an interesting perspective on the delivery of human development in specific economies – whether it is for everyone, or perhaps just for some. If one looks at figures for the USA, one sees a very high GDP per capita, but relatively low-performance GINI coefficients, as well as low-performance indicators for equality of education and health care for the poor. Where many developing countries have challenges in providing educational and healthcare services in rural areas, the USA has been described as having problems in that regard with some rural areas depopulating with respect to medical professionals and other facilities, but also of some attractively situated rural communities gentrifying with high income telecommuters. On the other hand, some urban areas of the USA suffer from violence – even in schools – which is one factor behind some urban demographic groups being challenged for dedicated teachers, and for poor results of their students. On the other hand, economies like Japan, Singapore, and Taiwan Province of China, tend to have much more egalitarian income, educational and health-care access, despite (with the very recent exception of Singapore) having more modest GDP per capita at purchasing power parity. Table 1 should be considered an experiment in trying to characterize egalitarian HDI. The only guarantee of such early experiments is that such first drafts are imperfect. Should the chosen indicators be scaled differently (e.g. non-linearly, or linearly with truncated tails)? Should we experiment with various techniques – such as histogram equalization stretches to data, before combining them in a non-linear, weighted combination? Further assessment, and more widespread discussion, might lead to improvements in socio-economic indicator development, and in understanding on how to positively use such indicators. Similarly, table 1 is clearly non-global. World Economic Forum efforts are increasing in geographic coverage, but more recent Global Competitiveness Reports dropped the Executive Opinion Survey question on equitability of access to education. One recommendation from this study is that such a question should be reinstated into the EOS.
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Figures 1 through 6 present maps of Income, Literacy, Life Expectancy at birth, the Basic HDI, Equitability-Inclusiveness Index and the Enhanced HDI for covered economies in Asia and the Pacific. Figure 1. Prototype Income component of HDI
Figure 1 indicates that annual incomes of about $12,000, $5,000, and $2,750 per capita at purchasing power parity mark transitions between top quarter, top half, top three quarters, and bottom quarter in Asia and the Pacific. Figure 2 indicates that literacy rates of about 99 per cent, 95 per cent and 90 per cent mark such quartile transitions in the region. Figure 3 indicates that Life Expectancies of 72.8, 70.2, and 65.3 years mark such quartile transitions in the region. It may be worth noting the tight clustering of literacy figures, as three quarters of regional economies exceed 90 per cent literacy rates (though they extend down to below 50 per cent). However, the ranges in incomes and life expectancies are higher, with resultant opportunity for national policies to consider opportunities for possible improvements. The quartile values for life expectancy are somewhat close together, also, though the total range of 43 years to 83 years is still rather wide in the region. Hastings (2009) offers some initial discussions on how to use HDI and its components to help economies to set their own developmental policies.
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Figure 2. Prototype literacy component of HDI for Asia and the Pacific
Figure 3. Prototype Life Expectancy component of HDI for Asia and the Pacific
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Figure 4. Prototype Basic Human Development Index for Asia and the Pacific
Figure 5. Prototype Inclusiveness Index for Asia and the Pacific
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Figure 6. Prototype Enhanced (inclusiveness-sensitive) HDI for Asia and the Pacific
2.4 Discussion Only twelve economies/societies rank above 0.8 in equitability/inclusiveness; thirteen more rank above 0.6 in this indicator, as seen in table 1. Fully 37 economies/societies, essentially half of the 75 samples with adequate data, rank below 0.5 – making these “least equitable/inclusive societies.� The USA and Greece are the two countries with Basic HDI values (Hastings 2009) above 0.9, but also with equitability/inclusiveness values below 0.5. Twenty economies/societies rank above 0.8 in Enhanced (inclusivenessenriched) Human Development Index, as shown in table 1, so could be considered relatively developed in terms of this indicator. Forty nine economies/societies rank between 0.5 and 0.8, so could be considered as mid-range in development; while six economies rank below 0.5, so could be considered as least developed in terms of this indicator. In almost all cases, economies/societies on table 1 perform less well in equitability than in raw (basic) HDI. Indeed, only Iceland does comparably well in both categories, though most of the highest-ranking economies/societies have narrower gaps between equitability/inclusiveness and raw (basic) HDI than those that rank lower. The biggest gaps are exhibited by Chile, Brazil, Mexico, the Philippines, Panama, Venezuela, Colombia, Peru, and the USA.
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3. A PROTOTYPE HUMAN SECURITY INDEX The prototype Inclusiveness Index and Enhanced HDI attempt to enhance the Basic HDI in the direction of characterizing human security – specifically regarding inclusiveness (which presumably also imparts a feeling of harmonious social balance in a society). Can we go farther to craft an indicator on human security? What would we characterize in such a task? For this study, I looked at indicators that may serve as proxy descriptions of various aspects of human security – which also include fairly large numbers of countries/economies. The study considered that human security is a sense that society is fair and just (e.g. not corrupt); a sense of harmony with the physical, social and spiritual environment lacking in organic circumstances that contribute to global, civil or domestic violence (verbal, mental, physical or otherwise by either gender); and that people are empowered with knowledge, honest and supportive information, financial benefits and opportunity, and resources to support a long and healthy life. In addition, where many indices on quality of life, etc. are advocated by Western-oriented groups, we should ask ourselves – how might a relatively globally balanced index be constructed, and how might it compare with indices currently being advocated? Commentators have lamented the dearth of good data that could contribute to a Human Security Index. I think that initial ingredients of a Human Security Index now exist. Table 2 (at the end of this report) offers a prototype HSI for some 200 economies11. 3.1 Constructing a Human Security Index12 Fortunately for such efforts at indicator development, groups of researchers have been pursuing the development of indices like the Gender Gap Index (World Economic Forum 2007), the Global Peace Index (Vision of Humanity 2008), World Prison Population List (Walmsley 2006), the Environmental Sustainability Index (Yale-Columbia Universities 2005), Environmental Performance Index (YaleColumbia Universities 2008) and compilations of greenhouse gas emissions13, World Bank's governance and freedom from corruption indicators (Kaufmann and Vicente, 2005; World Bank, 2008), World Telecommunication Indicators (ITU 2008) and the Press Freedom Index (Reporteurs Sans Frontieres 2007), as well as data that go into a geographically extended Basic Human Development Index (Hastings 200914, and column 8 in table 2). Table 2 offers derived, scaled 0.000-1.000 as in the HDI, component indices based on the data just cited. Components of table 2 were computed as described below: Column 2's Gender Equality Index was scaled from World Economic Forum (2007):
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Though this is about 30 economies fewer than I can compute a basic Human Development Index for (as done in the first part of this ESCAP Working Paper), it is nevertheless over 20 economies more than receive a HDI value in the most recent UNDP Human Development Report (UNDP, 2007). 12 Note to readers: This section is for reference, and may be skipped unless you want to know some details on how the index and its components are sourced and computed. 13 This study generally uses the list of greenhouse gas emissions per capita in Wikipedia for this topic. 14 Hastings (2009) covers over 230 economies – compared to the longstanding plateau in UNDP's HDRs of ~177 economies.
12
Gender Equality Index = GEI = (WEF Gender Gap Index – 0.45)/0.37
(4)
Column 3's Peace Index was scaled from Global Peace Index (Vision of Humanity 2008), the World Prison Population List (Walmsley 2007a), and complementary World Pre-trial/Remand Imprisonment List (Walmsley 2007b):
Scaled Global Peace Index = SGPI = (1-(Global Peace Index – 1.3))/2.2
(5)
Total Incarcerated = TI = (World Prison Population List + World Pre-trial&Remand Imprisonment List) (6)
Scaled Incarceration Index = SII = (910 - TI)/909 Peace Index = (SGPI + SII)/2
(7) (8)
Column 4's Environment Index was scaled from the Environmental Performance Index EPI, 2007), the Environmental Sustainability Index (ESI 2007), and greenhouse gas emissions (GGE) (Wikipedia 2008): Environmental Index = EI = Average(Average15(scaledEPI,scaledESI),GGE)
(9)
Column 5's Corruption Control Index was scaled from World Bank Institute governance data for illegal corruption (IC) and legal corruption (LC) percentiles as: Corruption Control Index = CCI = MINIMUM(IC, LC)
(10)
Column 6's Information Empowerment Index is a blend of the Connection Index (Hastings 2006 and 2008) which uses World Telecommunication Indicators (ITU, 2008) for Telephone Fixed Lines (TFL), Telephone Mobile Lines (TML), and Internet users (IU) (all as a per cent of the population) with the Press Freedom Index (RSF, 2008): Connection Index16 = CI = (TFL + TML)/2 + IU
(11)
Information Empowerment Index = IEI = Average(CI/200, scaled Press Freedom Index) (12)
Column 7's Social Fabric Index, which attempts to describe the “social fabric” 15
Though the spreadsheet function is “average,” this is actually the arithmetic mean of the input values. The Connection Index typically has values of 0-200, but can exceed 200 if Internet usage is high (e.g. over 70 per cent in some economies) and mobile phone usage exceeds 1 SIM card per user. Mobile phone usage exceeds 140 per cent of population in some economies, as fixed and Internet usage could theoretically exceed 100 per cent if many people had office and home phones and Internet accounts. 16
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of a society, is the unweighted mean of the five indices in columns 2 to 6 of table 2. When given an unweighted average with the Basic Human Development Index of column 8 in table 2 (Hastings 2009), we arrive at the prototype Human Security Index: Social Fabric Index = SFI = Average(GEI, PI, EI, CCI, IEI)
(13)
Human Security Index = HSI = Average(SFI, Basic HDI)
(14)
3.2 Discussion of the Social Fabric Index and the Human Security Index Table 2 includes 20+ countries more than UNDP covers in its Human Development Index. Hastings (2009), upon which column 8 in Table 2 is drawn, provides a Basic HDI for 232 economies (plus European Union and World averages), where the latest UNDP Human Development Report UNDP (2007) only offers 177 economies plus regional and global averages – a longstanding plateau for the UNDP HDR. Table 2 only shows scaled component scores for gender equality component (based on World Economic Forum, 2007 and equation 4), peace component (based on Vision of Humanity 2008, Walmsley 2007, and equation 8), environmental component (based on Yale-Columbia Universities 2005 and 2008, and Wikipedia 2008, and equation 9), corruption control component (based on Kaufmann and Vicente, 2005., World Bank, 2008, and equation 10), information empowerment (based on an updated version of the Connection Index of Hastings, 2006, and the Press Freedom Index of Reporteurs Sans Frontieres, 2007, and equation 12). The mean of those five scaled components create the Social Fabric Index (column 7, table 2). The mean of the SFI and the Basic HDI (column 8, table 2 and Hastings 2008) equate to the Human Security Index (column 9, table 2). Of the five components in the prototype Social Fabric Index: 114 economies have indicators in all categories, 35 economies lack one indicator, 33 economies lack two indicators, and 18 economies lack three indicators. Twenty one economies lack four indicators and thirteen economies lack all five indicators, so are omitted from the table. Considering that this is an ad-hoc initial effort, this situation is rather better than a “realist” might have thought. Though one may argue against the computation of a HSI for any economies which lack one or more constituent indicators, such a computation is offered here (with a grain of salt), in order to encourage the institutions that produce input indicators to strive for more geographic inclusiveness, where possible. Combining the World Peace Index with Walmsley's (2006) World Prison Population List is an experiment – but one using two potentially valuable proxy datasets relating to peacefulness and harmony in a society. A low prison population per 100,000 people could mean (a) a peaceful society with few serious crimes meriting imprisonment being committed (e.g. a good harmonious society in this regard); (b) a governing process that does not manipulate the policing and court systems to threaten or intimidate parts of populations (e.g. good lack of demographic bias in this regard); or (c) weak enforcement even if serious crimes are committed 14
(e.g. a problem that seems rare17 , as reputations of corrupt police/court processes tend to depict poorer people not being able to escape incarceration in such manners as richer people might be able to afford to do). High prison populations may indicate (a) high incidences of serious crime (e.g. unfortunate disharmony in such a society); (b) unfortunate manipulation of police/court processes to threaten or intimidate segments of a population; or (c) inappropriate/inequitable use of incarceration where other solutions may be more appropriate to solve a socio-economic challenge in a society (e.g. if a particular demographic cross-section is more vulnerable to incarceration). Further study of this indicator is warranted for this purpose – it is placed in this paper to stimulate possible discussion (and possible engagement toward improving it for use in a Human Security Index). Table 2 shows that the Social Fabric Index ranges in value from 0.099 (in Somalia) to over 0.850 (in four Scandinavian economies). The Human Security Index ranges in value from 0.231 (in Somalia) to over 0.900 (in Bermuda and three Scandinavian economies). Table 2A shows that only ten societies rank above 0.8 in the Social Fabric Index, so could be considered relatively developed societies in this regard. Fully 128 societies fall into the mid-range, while 62 societies rank below 0.5 in the Social Fabric Index, thus arguably being lesser-developed societies in terms of this indicator. It should be noted that several societies, which may lack one or more component indicators, may do better or worse as such component indicators may be developed. Likewise, further discussion and refinement of components and scaling formulae of the Social Fabric Index are likely to change outcomes. The raw (basic) HDI values exceed SFI values for most economies/societies. The exceptions tend to be African or island states. This pattern is also noted in the Happy Planet Index by Marks and others (2006). Perhaps, some assessment of reasons behind this may be useful for more materially-oriented societies which may wish to pursue social development. Table 2B shows that 35 societies rank above 0.8 in the Human Security Index, so could be considered relatively developed in this regard. Thirty six societies rank below 0.5, so could be considered as lesser-developed in this regard. The remaining 129 societies fall into the mid-range. Very few societies have higher Human Security Index values than they have for basic HDI. Those exceptions are African and small island societies, with the exception of Afghanistan and Bhutan. Perhaps Bhutan's policy of pursuing “Gross National Happiness18” is working. Relative areas of weakness of several (but hardly all) Asian-Pacific economies include perceptions of corruption, and the press freedom subcomponent of information empowerment. As a perception of low corruption control may harm investment and partnerships even if such perception is biased and inaccurate – it may 17
However, a perusal of Walmsley's (2007) data (prisoners per 100,000 population) suggests that, where many states with reputations for good governance have relatively low prison population densities, several of the lowest prison population densities are noted for economies for which one might wonder “what causes the rate for that country to be so low?” This aspect needs to be addressed. The author has a draft adjustment in mind, for possible improved use of that indicator in a human security index, but leaves this for future development. 18 http://en.wikipedia.org/wiki/Gross_National_Happiness
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benefit economies with perceived low levels of corruption control to work to improve such perceptions. Similarly, a reputation for less than optimal freedom of the press may hamper international partnerships. Moreover, recurrent financial and other scandals indicate that even perceived free press economies still lack enough investigative services in their governance in their key institutions – including in their traditional and 21st century media. Thus the Press Freedom Index might be good to supplement with a press effectiveness index if such could be crafted well. On the other hand, relative areas of strength vary between Asian-Pacific economies. Several have relatively good overall environmental scores (though there hardly exists any community with NO environmental problems/challenges to overcome). Several more have relatively good overall marks on the Global Peace Index. Indeed, the diversity of strengths offers Asian-Pacific planners opportunities for developing alternative candidate strategies for possible comparison and implementation to enhance human security (and not merely material development for GINI Coefficient winners19). Figures 7 and 8 map these two indicators for Asia and the Pacific. To emphasize, the SFI and HSI are experimental indices. As a result, table 2 shows no rankings (as one could imply data imperfections or weighting imperfections as much as one could imply support for social fabric or human security – at this early stage of indicator development). Many economies are listed for which one or more source indicators do not exist – due to lack of current coverage of those economies by the organizations creating those specific indicators. The partial rankings are nevertheless provided, as an implied encouragement for developers to extend the geographic coverage of their works.
19
“GINI Coefficient winners” here mean the small number of rich and influential people that benefit most when governments let economic growth for the rich dominate over broad-based development for everyone.
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Figure 7. Prototype Social Fabric Index for Asia and the Pacific
4. ADDITIONAL DISCUSSION 4.1. General discussion This idea of an inequality-adjusted HDI is not new. Hicks (1997) proposed a method of computing a “GINI coefficient” for income, education and health. He computed such indices, and a resultant Inequality-Adjusted HDI, for twenty developing countries, including seven in Asia and the Pacific. He found strong positive correlations between HDI, income, literacy, and life expectancy, and strong negative correlations between HDI, literacy inequality and life expectancy inequality, and essentially negligible positive correlation between HDI and income inequality. In short, as expected, inequality of literacy and/or life expectancy has a negative impact on HDI. My main question over Hicks' methodology is the use of the GINI computation, which (a) arbitrarily chooses certain percentiles within a population (e.g. dividing the distribution curve into quintiles as is common with income figures – but where I believe considerable experimentation should be done on the sizes of the two tails [e.g. As noted above, 10 per cent or 5 per cent, at the top end of the parameter distribution, and perhaps 40 per cent – 70 per cent at the bottom end of the curve]). Somewhat similarly, Grimm and others (2008) propose directly computing HDI values tagged to different income levels within an economy. Rather than computing GINI coefficients according to arbitrary cutoffs in parameter distribution curves, they propose compiling data, based on household surveys, specifically for literacy and life
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expectancy according to different levels of income, such as for the richest and poorest quintiles, within an economy. They present sample values for two developed countries and thirteen developing countries (including only Indonesia and Viet Nam in Asia and the Pacific). Figure 8. Prototype Human Security Index for Asia and the Pacific
In both cases the authors state or imply the difficulty in compiling data of comparable dates for many countries. This paper, by attempting to utilize data already compiled for relatively large groups of economies, has been able to present an in/equality-adjusted HDI for 75 economies. The ultimate goal is to include the maximum number of countries, by finding indicators which support such geographic robustness, and encourage more thematic (and indicator development) analysis and activity. Christopher Kuonqui20 (2008, verbal communication) has suggested that this paper consider renaming the Social Fabric Index to my proposed candidate for Human Security Index, and to keep the HDI out of the computed Human Security Index. This is an intriguing thought. Actually, I would prefer to incorporate the Extended HDI with the SFI to compute a HSI – but currently use the Basic HDI for want of greater geographic coverage of Extended HDI. Nevertheless, various options along these lines are hopefully worth a broader discussion, leading to a HSI with broader input (and presumed resultant usefulness). 20 Formerly of the Human Development Report Office, UNDP, with United Nations ESCAP when this paper was prepared.
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4.2. Discussion on each constituent indicator 1. Gender Equality Index (proxy for security with respect to diversity) My long term hope is that a more inclusive indicator could be crafted to represent the comfort (or lack of same) of an individual in a community, based on gender, race, ethnicity, age, religion, or other factor that could be (but hopefully is not) marginalizing – such as physical or other “disability.” Currently, though others attempt to cover such issues, I am using the World Economic Forum's Gender Gap Index as one with several components, which also makes a good start at global “completeness” by covering many economies. Also, it appears to be amenable to conservative, as well as more radical, interpretation-discussion-strategizing-policiesimplementations. This is important, as a successful Human Security Index should facilitate a diversity of discussion, intercomparison of alternative strategies, and implementation plans which help everyone -especially those currently on the shorter sides of the “Security Divide” between the “Privileged”, the “Semi-privileged and sort-of OK”, and the “Marginalized and not OK” corners of society. 2. Peace Index (proxy for secure peace and harmony in the community) The Global Peace Index is an excellent start at characterizing a person's peace and harmony in one's local and global community based on freedom from external warlike behaviour of one's country, and from domestic strife and related concerns. A “trigger-happy” society (overseas or at home) is not a secure one, and (as others have said) might become a magnet for trigger-happy behaviour of various sorts. Indeed, the argument by many of the “gun lobby” in the USA, arguing against bans or restrictions on gun ownership is that the country is so unsafe that people must be able to defend their own homes with their own guns. Is this not a graphic indicator of poor social fabric? I have also included incarceration rates as an excellent indicator of societal strains from criminal inclinations in a society, and/or inclinations of that society to incarcerate people rather than to fundamentally solve the possible causes of behaviour that result in arrest and detention. As Walmsley (2001 and other publications) notes, high incarceration rates can have many causes. However, in the view of several socioeconomic experts with whom I have discussed this issue, abnormally high incarceration rates indicate poor human development/security in a society, and appear to be an excellent proxy for societal fabric in great need of repair. Walmsley (2001) gives a thoughtful review of issues and concerns about high and generally increasing incarceration rates. Indeed, to me, the apparent geographic, temporal, and demographic relationships between increasing income inequality (e.g. GINI coefficient values) and increasing incarceration rates over the past two decades could make a valuable study. Janet Bilson Mancini (2006 and verbal communication) has an interesting discussion of well-being from a women's standpoint. She has suggested that domestic violence would be an invaluable, though very challenging, phenomenon to characterize through some form of indicator. I would be tempted to place such an indicator in this grouping on peacefulness, though some might argue that domestic violence is a gender – or even primarily a women's issue. However, if such an indicator were to actually appear, its specific characteristics would be the best guide
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to its possible grouping with other constituent indicators of a Social Fabric Index or a Human Security Index. 3. Environment Index (proxy for security via environmental stewardship) There are innumerable environmental indicators. My selection of indicators to be included is partly an attempt to be somewhat conservative and inclusive of environmental topics. One school of thought appears mostly focused on greenhouse gas emissions, or “carbon-equivalent footprint.” I certainly recognize the importance of that issue, as can be seen by the makeup of this Environment Index. However, a small carbon footprint resulting from a low level of HDI and energy use – but with a high level of chemical or biological contamination of water supply, a weak waste reusage infrastructure, etc. -is this an indicator of better environmental performance? Or might it be a possible suggestion of an environmentally disastrous future if that economy, say, suddenly strikes oil and begins big-time production – with concurrent massive inflows of oil revenues? I am intrigued by much of the discussion of Marks et al. (2006). Indeed, the Social Fabric Index also suggests that several African and island economies, and Bhutan (home of the concept of Gross National Happiness) among others are achieving (relatively unnoticed without such re-looks) relatively good results in the Social Fabric Index (e.g. development of true community) – which are comparably better than many countries which have placed greater emphasis on economic development (at least for the economic “haves” in those societies). Similarly, in the UNDP Thailand Human Development Report for 2007 (UNDP 2007), northeastern Thailand – despite its relative material poverty compared to most of the rest of the country – is a winner in achieving arguably a more profound sense of community. However, I am reluctant to use the Happy Planet Index as now constituted as part of the Social Fabric or Human Security Index. Can the Ecological Footprint, as used in the Happy Planet Index, be re-crafted for more broad-based acceptance? Could a life satisfaction indicator be developed to (1) first educate respondents more about the global and local situations before asking their (previously naïve, but afterward better informed) views? Could it (2) be crafted to minimize cultural interpretational diversity problems as implied in Marks et al. (2006)? If I am chagrined and saddened by the energy-wasteful lifestyle of my country – and that I am almost forced to own a car for each adult in my family because of lamentable lack of good public transport almost everywhere in my country (for example); but if I am also proud and excited by some of my country's progress in other aspects of environmental stewardship as well as by my own super-insulated solar-heated home – how should I respond to the Happy Planet Index survey? Actually, there appears to be no correct answer to that survey as currently crafted, when one has a sufficiently detailed view of the state of the planet. 4. Corruption Control Index (proxy for security that the community is fair) People feel more secure when special interests do not use subterfuge to derail governmental or other policies and activities aimed to support “life, liberty, and the pursuit of happiness.” Traditionally, one might attribute such subterfuge to corruption. Though the most well-known indicator of corruption may be Transparency
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International's Corruption Perceptions Index21, this effort uses the World Bank index on Control of Corruption22, including traditionally considered “illegal” corruption, plus the embryonic concept of “legal” corruption (Kaufmann and Vicente 2005). Anything which un-levels the playing field in favour of insiders or an “oligarchy” which costs the society as a whole, contributes to reduced human security. Though corruption is often thought of as associated with abuse by individuals who may use situations or positions for inappropriate monetary gain, this research considers that a wider process may be involved. For example, “the established way of doing things”, which may be institution-centred rather than service-oriented for the people, and no longer appropriate with new approaches available, may be just as negative an impact on human security. For example, imagine having your water or credit card bill managed as the USA Internal Revenue Service “administers23” taxes under its responsibility. Now imagine a taxing authority that administers taxes like your water utility or credit card issuer. You receive an accounting, with an opportunity to review and correct it for possible mistakes. Then you settle the bill (perhaps with a direct bill payment from your bank account). The latter can be done, and indeed is being done in well-run administrations. So maintaining a system that does not smoothly deliver good governance while also providing as reasonable a service as possible for the people may be viewed as corrupt, and degrading to human security. Also, an extension to characterize the effectiveness of traditional and new media in exposing corruption and other bad governance would be a great enhancement to a social fabric index. 5. Information Empowerment Index (proxy for security through empowerment of information – delivered through community-centric as opposed to special-interestcentric or commercially-muzzled information) People currently communicate in-person, or via (fixed and mobile) telephone, (postal or electronic) mail, and the Internet. The latter was formerly dominated by one-way communication from server to client24, but now is two-way through blogs, wikis, discussion forums, virtual community and file sharing media on the Web. Access to such media, and the freedom of such media from propaganda, intentional or unintentional bias (which limits rather than stimulates perspective) are vital to a social fabric. An indicator which characterizes the amount of access by individuals and communities to information, which is empowering of the people rather than of an elite, is an important component of social fabric and human security. This might include an enumeration of the number of people accessing various types of communications/media (not just phones and the Internet as in the case of the current list), and the freedom of such media (not just the press as at present). 21
http://www.transparency.org/policy_research/surveys_indices/cpi www.worldbank.org/wbi/governance and http://info.worldbank.org/governance/wgi/pdf/wgidataset.xls 23 This is characterized by dozens of forms and manuals, arcane “accounting” in each institution's own institution-centric bureaucratic manner; you get fined or jailed for a mistake or omission (“in your favour”) but pay more for a mistake or omission (“in their favour”). 24 Generally, radio and television broadcasting and print media are also one-way communication from producer to consumer, despite call-in broadcasts and reader submissions to newspapers and magazines. 22
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5. CONCLUSION As with the Normalized Difference Vegetation Index (NDVI) (Hastings and Emery 1992, Malingreau and Gregoire 1996, Tucker 2002, Hastings 2005) as an attempt to characterize vegetative health or drought, the HDI is an attempted proxy for societal development. Improved design of observations, synoptic views from afar (e.g. observing the Earth from space), data screening and compositing to improve desired signal over random or systematic noise distractions, and normative analysis, are leading to improved abilities of such proxy data to characterize drought or other phenomena for non-specialist decision-makers. Improved data stewardship (including editing and documentation), and new indicator development efforts can similarly lead to improved characterization of societal development, including human security. It is hoped that the prototypes offered here may facilitate discussion, and improvements, toward more robust proxy data toward these ends. The companion paper (Hastings 2009) to this one offers an approach to greater geographic inclusivity of HDI data, and feeds this paper with the data posted in column 8 of tables 1 and 2. The five components, derived in section 3.1 and discussed in section 4.2, will hopefully help stimulate further discussion, and improvement of this approach to developing a Human Security Index. I hope that the developers of indices used here, and potential alternatives or supplements along the lines discussed in section 4.2, can pursue their indicator development, with a focus on (1) enlarging geographic coverage to more economies – and to not omit small economies because of their valuable perspective on our global situation, (2) strengthening the preparation and documentation of their respective indicators for greater credibility and broader applicability, and (3) exploring their own perspectives on the roles of their indicators in describing their particular aspect(s) of social fabric and human security. I had originally wanted to develop an index, in modular fashion, that could be combined with the Human Development Index – or left by itself – to characterize the broad issue of human security. What resulted from this original approach was an index which attempts to characterize the fabric of a society – e.g. The Social Fabric Index. When the HDI is enhanced to cover the inclusiveness of the raw resources described by income, education, and health-care – it also becomes a better characterizer of one aspect of Human Security. Thus, the combination of the Social Fabric Index with the (hopefully Enhanced as in table 1 of this report) Human Development Index can form a rather comprehensive view on Human Security. In any case, the Social Fabric Index is considered here to be a useful indicator by itself, or it may be used in combination with either the Basic or the Enhanced HDI. Characterizing the inclusiveness of opportunity and basic services is considered here to be one aspect of characterizing human security. Thus, if the Enhanced HDI could be computed for more countries (e.g. if future Global Competitiveness Reports could include the questions on equitability of access to education and health-care), the author would propose replacing the Basic HDI with the Enhanced HDI in column 8 of table 2 – and in computing the Human Security Index. It may be worth making one additional point: One of the comments about the original Human Development Index is that, despite adding health and education to wealth (to some people) in an economy, that the statistical correlation between the
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HDI and its non-economic components to GDP per capita, and the year-to-year growth/decrease in HDI for an economy, is strongly correlated with GDP per capita, and its fluctuations between years. However, this paper demonstrates that the correlation between enhanced HDI, as well as the Social Fabric and Human Security Indexes are far less related to GDP per capita – and far more related to dedication and competence of governments, and other compelling circumstances such as governance of non-governmental aspects of a society, environmental and social history and practice in a society. The HDI leaders may not be leaders in human security – unless they work at it (and some appear to be working at this better than others).
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8. REFERENCES Cahill, M. B. (2005). “Is the Human Development Index redundant?” Eastern Economic Journal; vol.31, pp. 1-6. Central Intelligence Agency (2008). World Factbook. Washington, DC., USA, accessed from http://www.cia.gov/cia/publications/factbook. Commission on Human Security (2003). Human Security Now: Final Report, (New York, United Nations). Accessed from http://www.humansecuritychs.org/finalreport/index.html Economist Intelligence Unit (2005). “ The economist intelligence unit's quality-of-life index.” Economist: The World in 2005, pp. 1-4. Grimm, Michael, Kenneth Harttgen, Stephen Klasen, and Mark Misselhorn, (2008). “A human development index by income groups”. World Development, Elvesier, vol. 36(12), December issue, pp. 2527-2546. Hastings, David A., and William J. Emery (1992). “The advanced very high resolution radiometer (AVHRR): A brief reference guide”. Photogrammetric Engineering and Remote Sensing, vol. 58, pp. 1183-1188. Hastings, David A. (2005). “Africa's climate observed: perspectives on monitoring and management of floods, drought and desertification”, in Pak Sum Low, ed., Climate Change and Africa (Cambridge University Press), pp. 50-59. Hastings, David A. (2008). “Describing the human condition – from human development to human security: an environmental remote sensing and GIS approach”, in Proceedings, GIS-IDEAS 2008, accessed from http://wgrass.media.osaka-cu.ac.jp/gisideas08/viewabstract.php?id=299 Hastings, David A. (2009). “Filling gaps in the Human Development Index: Findings for Asia and the Pacific”. UNESCAP Working Paper, WP/09/02. Hicks, Douglas A. (1997). “The inequality-adjusted Human Development Index: a constructive proposal”. World Development, vol. 25, no. 8, pp. 1283-1298. ITU
(2008). World Telecommunications Indicators, accessed http://www.itu.int/ITU-D/icteye/Default.aspx , (Geneva, ITU).
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(Washington DC, World Bank Institute). Kalmijn, Wim and Ruut Veenhoven (2005). “Measuring inequality of happiness in nations: In search for proper statistics”. Journal of Happiness Studies, vol. 6, pp. 357-396. Mancini Bilson, Janet and Carolyn Fluehr-Lobban (2006). Female Well-being: Toward a Global Theory of Social Change (New York, Zed Books). Marks, Nic, Saamah Abdallah, Andrew Simms and Sam Thompson (2006). The Happy Planet Index, accessed from http://www.happyplanetindex.org, (London, New Economic Foundation). Malingreau, J. P. and J. M. Gregoire (1996). “Developing a global vegetation fire monitoring system for global change studies: a framework”, in J. S. Levine, ed., Biomass Burning and Global Change. (Cambridge, Massachusetts, USA, Massachusetts Institute of Technology Press), pp. 14-24. Prescott-Allen, Robert (2001). The Wellbeing of Nations, (Ottawa, Ontario, International Development Research Corporation), p. 350. Reporteurs Sans Frontieres (2007). Press Freedom Index, 2007 accessed from http://www.rsf.org/article.php? id_article=24025. Tadjbakhsh, Shahrbanou (2008). “Human security”, Human Development Insights, Issue 17, accessed from http://hdr.undp.org/en/nhdr, (New York, United Nations Development Programme HDR Networks). Tucker, C. J. (2002). Drought Africa, accessed from http://svs.gsfc.nasa.gov/stories/drought/africa.html, (USA, National Aeronautics and Space Administration). UNDP (1990). Human Development Report. (New York, UNDP). UNDP (2007). Thailand National Human Development Report, 2007, accessed from http://hdr.undp.org/en/reports/nationalreports/asiathepacific/thailand/name,3418 ,en.html (New York, UNDP). UNU-WIDER (2008). World Income Inequality Database, May, 2008, accessed from http://www.wider.unu.edu/research/Database/en_GB/database . Vision of Humanity (2008). Global Peace Index, accessed http://www.visionofhumanity.org/gpi/results/ rankings/2008 .
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Walmsley, Roy (2006). World Prison Population List, accessed http://www.prisonstudies.org , (London, Kings College London).
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Wikipedia (2008). Accessed from www.wikipedia.org . World Bank (2008). Global Governance and Corruption, accessed from http://www.worldbank.org/wbi/ governance/pubs/gcr2004.html, (Washiongton, DC., World Bank Institute). World Economic Forum (2002). Global Competitiveness Report 2001-2002, (New York, Oxford University Press), p. 344, tables 5.13 and 5.14. World Economic Forum (2007). Gender Gap Index, 2007, accessed from http://weforum.org/en/Communities/ Women%20Leaders%20and%20Gender%20Parity/GenderGapNetwork/index. htm . Yale-Columbia Universities (2005). Environmental Sustainability Index, accessed from http://www.yale.edu/esi/ ESI2005_Main_Report.pdf . Yale-Columbia Universities (2008). Environmental Performance Index, accessed from http://epi.yale.edu/epi/files/ 2008EPI_Rankings_1page.pdf.
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APPENDIX
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Table 1. Basic Human Development Index, Equitability and (Inclusiveness) Enhanced Human Development Indices, and their components Column 1 Economy Iceland Finland Austria Denmark Norway Netherlands Switzerland Sweden Germany Belgium France Japan Slovakia Slovenia Taiwan, Prov.of China Canada Czech Republic Singapore Australia Spain Israel Italy New Zealand Ireland Hungary Hong Kong, China United Kingdom Estonia Republic of Korea Lithuania Poland Portugal Greece Latvia Bulgaria United States Trinidad & Tobago Romania Costa rica Malaysia
C.2 C. 3 C.4 C.5 C.6 C.7 C.8 C.9 C.10 C.11 C.12 C.13 C.14 C.15 C.16 Income Scaled Lit. Scaled LE Scaled Basic GINI Scaled ED Scaled ED Scaled HC Equitability Enhanced HC Access Blend Income blend Lit. blend LE HDI blend GINI Access Access Access Index HDI Relatively high inclusiveness “enhanced” Human Development Index 37169 0.988 99 0.990 81 0.936 0.971 20 0.998 6.7 0.957 6.9 0.986 0.980 0.976 34875 0.977 99 0.990 79 0.899 0.955 27 0.862 6.8 0.971 6.2 0.886 0.906 0.931 37509 0.989 99 0.985 80 0.912 0.962 28 0.850 6.6 0.943 6.2 0.886 0.893 0.927 36644 0.985 99 0.990 78 0.890 0.955 24 0.921 6.3 0.900 5.9 0.843 0.888 0.921 50891 1.040 99 0.990 80 0.917 0.982 27 0.852 6.3 0.900 5.8 0.829 0.860 0.921 37644 0.990 99 0.990 80 0.910 0.963 28 0.831 6.4 0.914 5.8 0.829 0.858 0.910 40581 1.002 99 0.990 81 0.940 0.977 32 0.759 6.4 0.914 5.8 0.829 0.834 0.905 35657 0.981 99 0.990 81 0.930 0.967 25 0.900 5.3 0.757 5.8 0.829 0.829 0.898 33216 0.969 99 0.990 79 0.907 0.955 28 0.834 6.2 0.886 5.5 0.786 0.835 0.895 34686 0.976 99 0.990 79 0.900 0.955 29 0.820 6.1 0.871 5.6 0.800 0.830 0.893 32799 0.967 99 0.990 81 0.928 0.961 27 0.863 5.3 0.757 5.8 0.829 0.816 0.889 33553 0.971 99 0.990 83 0.958 0.973 32 0.770 5.5 0.786 5.7 0.814 0.790 0.882 19257 0.878 99 0.994 75 0.827 0.900 26 0.884 6.4 0.914 5.2 0.743 0.847 0.874 26240 0.930 99 0.993 78 0.875 0.933 28 0.832 6.1 0.871 4.8 0.686 0.796 0.865 30801 0.956 96 0.961 78 0.876 0.931 32 0.760 5.8 0.829 5.5 0.786 0.791 0.861 36570 0.985 98 0.980 81 0.925 0.963 33 0.743 5.2 0.743 5.4 0.771 0.752 0.858 23080 0.908 99 0.990 77 0.858 0.919 26 0.873 5.6 0.800 4.5 0.643 0.772 0.845 44928 1.019 93 0.934 80 0.922 0.959 44 0.515 5.6 0.800 5.6 0.800 0.705 0.832 34784 0.977 99 0.990 81 0.938 0.968 35 0.696 4.3 0.614 5.2 0.743 0.684 0.826 29536 0.949 98 0.975 81 0.927 0.950 34 0.728 4.1 0.586 4.8 0.686 0.666 0.808 Mid-level inclusiveness “enhanced” Human Development Index 26029 0.928 96 0.955 81 0.929 0.937 38 0.634 5.3 0.757 4.1 0.586 0.659 0.798 29963 0.952 99 0.987 81 0.925 0.955 35 0.700 4.7 0.671 3.6 0.514 0.629 0.792 25977 0.928 99 0.990 80 0.917 0.945 36 0.676 4.5 0.643 4.1 0.586 0.635 0.790 40765 1.003 99 0.985 79 0.896 0.961 35 0.698 4.8 0.686 2.8 0.400 0.595 0.778 18529 0.872 99 0.992 73 0.800 0.888 27 0.862 4.6 0.657 2.9 0.414 0.644 0.766 41296 1.005 94 0.938 82 0.947 0.963 48 0.433 4.6 0.657 4.3 0.614 0.568 0.766 34756 0.977 99 0.990 79 0.902 0.956 35 0.692 3.5 0.500 3.7 0.529 0.574 0.765 19616 0.881 99 0.994 72 0.781 0.885 36 0.684 4.8 0.686 3.7 0.529 0.633 0.759 24464 0.918 98 0.984 78 0.887 0.929 36 0.672 4.3 0.614 3.3 0.471 0.586 0.757 17148 0.859 99 0.994 72 0.782 0.878 34 0.725 4.7 0.671 2.5 0.357 0.585 0.731 15808 0.845 99 0.992 75 0.838 0.892 34 0.714 4.5 0.643 2.3 0.329 0.562 0.727 21277 0.895 94 0.940 78 0.886 0.907 37 0.655 4.1 0.586 2.5 0.357 0.533 0.720 28962 0.946 97 0.970 80 0.908 0.941 35 0.695 3.3 0.471 2.3 0.329 0.498 0.720 16608 0.853 99 0.995 72 0.781 0.876 35 0.701 4.0 0.571 2.6 0.371 0.548 0.712 10941 0.784 98 0.983 73 0.798 0.855 31 0.777 3.9 0.557 2.5 0.357 0.564 0.709 45020 1.02 96 0.955 78 0.885 0.953 51 0.380 3.2 0.457 3.6 0.514 0.450 0.702 16793 0.855 99 0.985 69 0.730 0.857 40 0.594 4.5 0.643 2.8 0.400 0.546 0.701 10931 0.783 98 0.977 72 0.790 0.850 31 0.780 3.6 0.514 2.0 0.286 0.527 0.688 10163 0.771 96 0.958 78 0.885 0.871 48 0.436 2.8 0.400 3.8 0.543 0.460 0.665 12944 0.812 90 0.900 73 0.805 0.839 47 0.459 3.6 0.514 3.5 0.500 0.491 0.665
Economy Mauritius Uruguay Jamaica Jordan Russian Federation China Ukraine Viet Nam Argentina Turkey Thailand Sri Lanka Indonesia Ecuador Egypt Chile Mexico Venezuela Panama Philippines Paraguay Colombia Dominican Republic Peru India Brazil El Salvador Bolivia Honduras Nicaragua Guatemala Bangladesh South Africa Nigeria Zimbabwe
Income Scaled Lit. Scaled LE Scaled Basic GINI Scaled ED Scaled ED Scaled HC Equitability Enhanced HC Access Blend Income blend Lit. blend LE HDI blend GINI Access Access Access Index HDI 11845 0.797 86 0.857 73 0.795 0.816 37 0.660 3.5 0.500 2.6 0.371 0.510 0.663 11292 0.789 98 0.977 76 0.848 0.871 45 0.502 3.1 0.443 2.9 0.414 0.453 0.662 6261 0.690 85 0.855 73 0.793 0.779 38 0.640 3.8 0.543 2.8 0.400 0.528 0.653 5183 0.659 92 0.916 74 0.809 0.795 38 0.648 2.8 0.400 2.9 0.414 0.487 0.641 14008 0.825 99 0.992 66 0.679 0.832 42 0.558 3.5 0.500 1.9 0.271 0.443 0.638 5824 0.678 92 0.918 73 0.795 0.797 44 0.513 3.1 0.443 3.2 0.457 0.471 0.634 7023 0.710 99 0.989 68 0.712 0.803 37 0.670 2.6 0.371 1.9 0.271 0.438 0.620 2753 0.553 92 0.921 72 0.783 0.752 37 0.669 2.9 0.414 2.2 0.314 0.466 0.609 13737 0.822 97 0.975 76 0.841 0.879 50 0.395 2.4 0.343 1.8 0.257 0.332 0.605 11727 0.795 88 0.877 72 0.788 0.820 40 0.600 1.9 0.271 2.0 0.286 0.386 0.603 8215 0.736 94 0.939 71 0.770 0.815 49 0.430 2.5 0.357 2.5 0.357 0.381 0.598 4325 0.629 92 0.919 73 0.796 0.781 49 0.430 2.9 0.414 2.7 0.386 0.410 0.596 3776 0.606 90 0.897 69 0.735 0.746 35 0.709 2.1 0.300 1.8 0.257 0.422 0.584 6508 0.697 92 0.921 75 0.831 0.816 43 0.543 2.0 0.286 1.5 0.214 0.348 0.582 5278 0.662 72 0.725 70 0.755 0.714 34 0.712 2.1 0.300 2.3 0.329 0.447 0.580 13373 0.817 96 0.964 78 0.883 0.888 55 0.291 1.8 0.257 1.8 0.257 0.268 0.578 12318 0.803 92 0.918 76 0.841 0.854 51 0.381 1.7 0.243 1.8 0.257 0.294 0.574 10955 0.784 93 0.932 74 0.811 0.842 47 0.468 1.6 0.229 1.3 0.186 0.294 0.568 9346 0.757 93 0.926 76 0.841 0.841 56 0.272 2.1 0.300 2.0 0.286 0.286 0.563 3960 0.614 94 0.935 70 0.755 0.768 46 0.478 1.8 0.257 1.8 0.257 0.331 0.549 4571 0.638 94 0.935 74 0.808 0.794 57 0.254 2.2 0.314 2.4 0.343 0.304 0.549 6926 0.707 93 0.931 73 0.799 0.812 57 0.262 1.9 0.271 2.2 0.314 0.283 0.547 6922 0.707 87 0.867 71 0.761 0.778 50 0.409 1.7 0.243 1.7 0.243 0.298 0.538 7366 0.718 89 0.89 71 0.770 0.793 52 0.354 1.6 0.229 1.6 0.229 0.270 0.532 2945 0.565 62 0.621 65 0.667 0.618 33 0.750 1.9 0.271 1.9 0.271 0.431 0.524 9424 0.759 89 0.887 72 0.786 0.811 58 0.240 1.8 0.257 1.5 0.214 0.237 0.524 5483 0.668 82 0.823 72 0.775 0.755 53 0.344 1.8 0.257 1.8 0.257 0.286 0.521 3665 0.601 87 0.875 66 0.675 0.717 53 0.350 1.8 0.257 1.9 0.271 0.293 0.505 3742 0.605 81 0.812 69 0.740 0.719 54 0.312 1.9 0.271 1.9 0.271 0.285 0.502 Relatively low inclusiveness “enhanced� Human Development Index 2777 0.555 77 0.766 71 0.770 0.697 49 0.429 1.6 0.229 1.7 0.243 0.300 0.499 4572 0.638 71 0.710 69 0.729 0.692 58 0.250 1.7 0.243 1.8 0.257 0.250 0.471 1517 0.454 48 0.476 63 0.638 0.523 32 0.764 1.5 0.214 1.5 0.214 0.398 0.460 10176 0.772 86 0.862 48 0.385 0.673 60 0.207 1.8 0.257 1.7 0.243 0.236 0.454 1775 0.480 70 0.695 46 0.357 0.511 47 0.457 1.7 0.243 1.8 0.257 0.319 0.415 813 0.350 90 0.904 41 0.259 0.504 66 0.081 1.8 0.257 1.8 0.257 0.198 0.351 Aggregates Mean for global economies 0.851 39.81 0.604 3.696 0.528 3.277 0.468 0.533 0.692 Mean for Asia- Pacific Mean for Africa Mean for Latin America Mean for Europe Mean for North America
0.835 0.601 0.801 0.924 0.958
39.47 51.75 51.26 30.79 42.00
0.612 0.364 0.375 0.783 0.562
3.559 1.850 2.068 5.076 4.200
0.508 0.264 0.295 0.725 0.600
3.347 1.900 2.010 4.200 4.500
0.478 0.271 0.287 0.600 0.642
0.533 0.300 0.319 0.703 0.601
0.684 0.450 0.560 0.813 0.780
Table 2A. Global Societies, sorted by Social Fabric Index Column 1
C.2
C.3
C.4
C.5
C.6
C.7
C.8
C.9 C.10 Basic Corruption InfoSocial Gender EnvironBasic Human Basic HDIEconomy Peace Control Empower- Fabric Equality ment HDI Security SFI Perception ment Index Index Societies with relatively high Social Fabric Norway 0.962 0.917 0.851 0.786 0.890 0.881 0.982 0.932 0.101 Saint Kitts & Nevis 0.945 0.816 0.881 0.853 0.867 -0.028 Iceland 0.903 0.975 0.858 0.696 0.887 0.864 0.971 0.917 0.107 Finland 0.957 0.899 0.873 0.726 0.849 0.861 0.955 0.908 0.094 Sweden 0.986 0.881 0.904 0.600 0.903 0.855 0.967 0.911 0.112 Cook Islands 0.964 0.723 0.843 0.829 0.836 -0.014 Bermuda 0.854 0.829 0.842 0.981 0.911 0.139 Denmark 0.816 0.916 0.772 0.747 0.860 0.822 0.955 0.889 0.133 Andorra 0.854 0.769 0.812 0.984 0.898 0.173 Netherlands 0.778 0.818 0.717 0.792 0.905 0.802 0.963 0.883 0.161 Societies with mid-range Social Fabric New Zealand 0.851 0.836 0.747 0.687 0.861 0.797 0.945 0.871 0.148 Barbados 0.891 0.850 0.642 0.794 0.900 0.847 0.106 Germany 0.843 0.873 0.756 0.624 0.835 0.786 0.955 0.871 0.169 Saint Lucia 0.945 0.830 0.579 0.785 0.828 0.806 0.043 Bahamas 0.873 0.908 0.564 0.781 0.881 0.831 0.100 United Kingdom 0.795 0.763 0.753 0.674 0.851 0.767 0.956 0.862 0.189 Switzerland 0.654 0.874 0.845 0.591 0.860 0.765 0.977 0.871 0.212 Saint Vincent & the Grenadines 0.964 0.816 0.500 0.760 0.811 0.785 0.051 Austria 0.692 0.870 0.827 0.572 0.806 0.753 0.962 0.858 0.209 Grenada 0.945 0.718 0.591 0.752 0.811 0.781 0.059 Cape Verde 0.982 0.728 0.509 0.740 0.725 0.732 -0.015 Antigua & Barbuda 0.545 0.854 0.809 0.736 0.835 0.786 0.099 Hong Kong, China 0.806 0.591 0.808 0.735 0.963 0.849 0.228 Ireland 0.800 0.898 0.737 0.426 0.803 0.733 0.961 0.847 0.228 Macau, China 0.694 0.753 0.723 0.933 0.828 0.210 Belgium 0.730 0.857 0.657 0.541 0.802 0.717 0.955 0.836 0.238 Canada 0.730 0.860 0.713 0.429 0.844 0.715 0.963 0.839 0.248 Portugal 0.665 0.872 0.796 0.420 0.792 0.709 0.907 0.808 0.198 Greece 0.581 0.777 0.731 0.689 0.759 0.707 0.941 0.824 0.234 Luxembourg 0.619 0.814 0.618 0.572 0.913 0.707 0.997 0.852 0.290 Bhutan 0.799 0.816 0.806 0.376 0.699 0.605 0.652 -0.094 Australia 0.730 0.807 0.652 0.503 0.784 0.695 0.968 0.832 0.273 Dominica 0.945 0.733 0.406 0.695 0.822 0.758 0.127 Spain 0.795 0.783 0.739 0.397 0.757 0.694 0.950 0.822 0.256 Japan 0.530 0.920 0.776 0.462 0.779 0.693 0.973 0.833 0.280 Seychelles 0.873 0.607 0.593 0.691 0.848 0.769 0.157 Singapore 0.570 0.704 0.745 0.726 0.651 0.679 0.959 0.819 0.280 France 0.627 0.817 0.786 0.399 0.764 0.679 0.961 0.820 0.282 Slovenia 0.632 0.884 0.769 0.273 0.808 0.673 0.933 0.803 0.260 Lesotho 0.697 0.964 0.578 0.413 0.663 0.526 0.594 -0.137 Estonia 0.678 0.680 0.662 0.409 0.874 0.660 0.885 0.773 0.225 Cyprus 0.546 0.798 0.828 0.367 0.746 0.657 0.935 0.796 0.278 Uruguay 0.570 0.733 0.919 0.389 0.670 0.656 0.871 0.764 0.215 Italy 0.541 0.817 0.748 0.344 0.827 0.655 0.955 0.805 0.300 Taiwan, Prov. Of China 0.702 0.634 0.446 0.828 0.653 0.931 0.792 0.278 Netherlands Antilles 0.854 0.450 0.652 0.886 0.769 0.234 Chile 0.535 0.725 0.795 0.535 0.661 0.650 0.888 0.769 0.238 Latvia 0.765 0.675 0.829 0.207 0.771 0.649 0.876 0.763 0.227 Croatia 0.732 0.767 0.810 0.185 0.743 0.648 0.889 0.768 0.241
Economy Lithuania Liechtenstein Czech Republic Cayman Islands Ghana Costa Rica Hungary Fiji Slovakia Korea, Rep. of Oman USA Virgin Islands Romania Senegal Bulgaria Kuwait Namibia Malaysia United Arab Emirates Guam Jordan Malta Macedonia, TFYR Argentina Botswana Jamaica Moldova Samoa Bosnia & Herzegovina Albania Maldives South Africa El Salvador Ecuador Mauritius Poland Brunei Darussalam Comoros Puerto Rico Bahrain Tanzania Montenegro Rwanda Israel Gabon Swaziland Colombia Tunisia Qatar Brazil Vanuatu Dominican Republic Ukraine VietNam
Gender EnvironPeace Equality ment 0.738 0.728
0.818
0.600 0.813
0.708
0.603 0.842 0.678 0.756 0.603 0.805 0.622 0.803 0.516 0.812 0.378 0.857
0.760 0.851 0.765 0.945 0.759 0.674 0.691
0.638 0.809 0.770 0.700 0.743 0.516 0.777 0.678 0.669 0.524 0.748 0.454 0.639
0.664 0.799 0.730 0.357 0.723 0.664 0.438
0.459 0.573 0.668 0.670 0.622 0.657 0.722
0.717 0.891 0.723 0.809 0.706 0.711 0.741 0.964 0.762 0.815 0.964 0.647 0.711 0.775 0.945 0.697 0.527 0.982
0.592 0.500 0.727 0.635 0.643 0.538 0.611
0.733 0.724 0.711 0.644 0.650 0.620 0.776 0.700 0.478 0.644 0.668 0.741
0.386 0.747 0.668 0.743
0.564 0.730
0.757 0.668 0.400 0.733
0.697 0.701 0.802 0.955 0.833 0.728 0.005 0.801 0.945 0.718 0.676 0.673
0.700 0.481 0.416 0.576
0.540 0.683 0.852 0.625
0.597 0.673 0.619 0.566 0.646 0.816
Basic Corruption InfoSocial Basic Human Basic HDIControl Empower- Fabric HDI Security SFI Perception ment Index Index 0.163 0.778 0.645 0.878 0.762 0.233 0.854 0.435 0.645 0.983 0.814 0.339 0.309 0.786 0.643 0.919 0.781 0.276 0.854 0.425 0.639 0.983 0.811 0.344 0.472 0.511 0.637 0.557 0.597 -0.080 0.243 0.638 0.633 0.871 0.752 0.238 0.225 0.766 0.633 0.888 0.760 0.255 0.456 0.482 0.628 0.774 0.701 0.146 0.179 0.772 0.627 0.900 0.764 0.273 0.310 0.807 0.624 0.929 0.776 0.305 0.748 0.415 0.618 0.843 0.730 0.225 0.757 0.474 0.615 0.894 0.755 0.279 0.195 0.757 0.612 0.850 0.731 0.238 0.417 0.459 0.611 0.482 0.547 -0.129 0.184 0.695 0.610 0.855 0.733 0.245 0.738 0.647 0.607 0.937 0.772 0.330 0.425 0.536 0.606 0.664 0.635 0.058 0.471 0.605 0.602 0.839 0.721 0.237 0.682 0.799 0.602 0.900 0.751 0.298 0.757 0.442 0.599 0.901 0.750 0.302 0.594 0.489 0.599 0.795 0.697 0.196 0.384 0.545 0.598 0.905 0.752 0.307 0.205 0.656 0.595 0.839 0.717 0.244 0.162 0.608 0.592 0.879 0.736 0.287 0.473 0.512 0.591 0.673 0.632 0.082 0.204 0.734 0.591 0.779 0.685 0.188 0.301 0.553 0.587 0.758 0.673 0.171 0.631 0.164 0.586 0.803 0.695 0.217 0.149 0.652 0.585 0.839 0.712 0.254 0.282 0.531 0.584 0.839 0.711 0.255 0.393 0.474 0.583 0.779 0.681 0.196 0.465 0.585 0.581 0.673 0.627 0.092 0.339 0.569 0.580 0.755 0.667 0.175 0.248 0.560 0.579 0.816 0.697 0.237 0.168 0.659 0.578 0.816 0.697 0.238 0.144 0.694 0.577 0.892 0.735 0.315 0.636 0.564 0.576 0.939 0.757 0.363 0.345 0.394 0.573 0.575 0.574 0.002 0.743 0.403 0.573 0.905 0.739 0.332 0.523 0.636 0.571 0.890 0.731 0.319 0.263 0.450 0.571 0.520 0.545 -0.051 0.398 0.742 0.570 0.832 0.701 0.262 0.558 0.255 0.567 0.478 0.522 -0.089 0.367 0.692 0.565 0.937 0.751 0.372 0.209 0.503 0.562 0.704 0.633 0.142 0.403 0.320 0.559 0.550 0.555 -0.009 0.222 0.496 0.558 0.812 0.685 0.254 0.488 0.403 0.557 0.764 0.660 0.207 0.786 0.723 0.557 0.939 0.748 0.382 0.199 0.560 0.552 0.811 0.682 0.259 0.626 0.077 0.549 0.672 0.611 0.123 0.220 0.527 0.547 0.778 0.663 0.231 0.225 0.622 0.542 0.803 0.672 0.261 0.291 0.281 0.542 0.752 0.647 0.210
Economy Peru Paraguay Uganda Mozambique Cuba Nicaragua Indonesia Philippines Malawi Belarus Panama Armenia Kiribati Mongolia Burkina Faso Bolivia Algeria United States Trinidad & Tobago Guatemala Venezuela China Sri Lanka Saudi Arabia Lebanon Djibouti Kazakhstan Madagascar Eritrea Egypt Serbia Zambia Morocco Sao Tome & Principe Syria Honduras Mexico Solomon Islands Mali Kenya Mauritania Georgia Thailand Turkey Congo, Republic of the India Tonga Azerbaijan Gambia Suriname Cambodia Cote d'Ivoire Kyrgyzstan
Basic Corruption InfoSocial Basic Human Basic HDIControl Empower- Fabric HDI Security SFI Perception ment Index Index 0.573 0.677 0.768 0.199 0.487 0.541 0.793 0.667 0.252 0.584 0.730 0.755 0.136 0.496 0.540 0.794 0.667 0.254 0.630 0.648 0.742 0.262 0.412 0.539 0.508 0.523 -0.031 0.643 0.804 0.653 0.171 0.422 0.539 0.374 0.456 -0.165 0.722 0.544 0.792 0.510 0.125 0.538 0.833 0.686 0.295 0.530 0.762 0.715 0.184 0.498 0.538 0.697 0.617 0.159 0.554 0.778 0.670 0.233 0.454 0.538 0.746 0.642 0.208 0.846 0.626 0.724 0.074 0.401 0.534 0.768 0.651 0.234 0.535 0.758 0.716 0.257 0.397 0.533 0.449 0.491 -0.084 0.705 0.489 0.732 0.214 0.521 0.532 0.832 0.682 0.300 0.662 0.554 0.748 0.124 0.569 0.532 0.841 0.686 0.309 0.581 0.711 0.350 0.482 0.531 0.803 0.667 0.272 0.982 0.592 0.017 0.530 0.739 0.635 0.209 0.603 0.597 0.597 0.374 0.472 0.528 0.733 0.631 0.205 0.381 0.779 0.643 0.413 0.422 0.528 0.368 0.448 -0.160 0.559 0.724 0.724 0.145 0.484 0.527 0.717 0.622 0.190 0.424 0.632 0.718 0.393 0.463 0.526 0.730 0.628 0.204 0.676 0.262 0.626 0.308 0.742 0.523 0.953 0.738 0.430 0.638 0.546 0.545 0.199 0.684 0.522 0.857 0.690 0.335 0.443 0.695 0.713 0.267 0.490 0.522 0.692 0.607 0.170 0.622 0.648 0.678 0.126 0.522 0.519 0.842 0.681 0.323 0.578 0.752 0.639 0.379 0.240 0.518 0.797 0.657 0.279 0.738 0.583 0.743 0.239 0.286 0.518 0.781 0.649 0.263 0.311 0.617 0.564 0.617 0.467 0.515 0.837 0.676 0.322 0.475 0.721 0.359 0.503 0.514 0.806 0.660 0.292 0.964 0.286 0.294 0.514 0.553 0.534 0.039 0.670 0.581 0.657 0.184 0.478 0.514 0.816 0.665 0.302 0.530 0.779 0.670 0.158 0.429 0.513 0.551 0.532 0.038 0.982 0.544 0.010 0.512 0.496 0.504 -0.016 0.354 0.760 0.683 0.404 0.345 0.509 0.714 0.612 0.205 0.711 0.215 0.592 0.506 0.854 0.680 0.348 0.484 0.756 0.607 0.228 0.446 0.504 0.482 0.493 -0.022 0.319 0.683 0.721 0.286 0.504 0.503 0.642 0.572 0.139 0.982 0.388 0.133 0.501 0.662 0.581 0.161 0.465 0.759 0.663 0.296 0.320 0.501 0.749 0.625 0.248 Societies with relatively low Social Fabric 0.584 0.592 0.748 0.112 0.451 0.497 0.719 0.608 0.222 0.524 0.615 0.699 0.222 0.424 0.497 0.854 0.675 0.357 0.982 0.490 0.018 0.496 0.647 0.572 0.151 0.411 0.725 0.644 0.246 0.456 0.496 0.357 0.427 -0.139 0.543 0.612 0.693 0.160 0.460 0.494 0.567 0.530 0.073 0.411 0.697 0.546 0.325 0.488 0.493 0.512 0.503 0.019 0.586 0.746 0.140 0.494 0.491 0.803 0.647 0.312 0.627 0.541 0.736 0.116 0.434 0.491 0.815 0.653 0.324 0.343 0.620 0.733 0.195 0.549 0.488 0.820 0.654 0.332 0.697 0.688 0.121 0.442 0.487 0.620 0.554 0.133 0.389 0.703 0.658 0.298 0.379 0.486 0.618 0.552 0.132 0.964 0.053 0.439 0.485 0.813 0.649 0.328 0.616 0.619 0.643 0.150 0.372 0.480 0.794 0.637 0.314 0.519 0.711 0.306 0.369 0.476 0.463 0.470 -0.013 0.619 0.524 0.270 0.471 0.793 0.632 0.322 0.500 0.728 0.638 0.073 0.414 0.470 0.605 0.538 0.135 0.669 0.681 0.083 0.438 0.468 0.456 0.462 -0.012 0.581 0.726 0.102 0.451 0.465 0.724 0.595 0.259
Gender EnvironPeace Equality ment
Economy Lao Peoples Demo. Rep. Russian Federation Cameroon Central African Republic Afghanistan Libya Bangladesh Iran, Islamic Republic Haiti Uzbekistan Equatorial Guinea Tajikistan Angola Togo Zimbabwe Nigeria Benin Ethiopia Liberia Guinea Bissau Guinea Pakistan Congo, Democratic Rep. of the Nepal Yemen Nigeria Guyana American Samoa Sudan Chad Papua New Guinea Burundi Sierra Leone Myanmar Iraq Micronesia, Federated States of Belize Timor-Leste Turkmenistan New Caledonia Korea, Dem. Peop. Rep. of Marshall Islands Palestinian Authority Somalia
Gender EnvironPeace Equality ment 0.818 0.641 0.243 0.384 0.655 0.580 0.534 0.658 0.489 0.739 0.378 0.594 0.675 0.654 0.615 0.642 0.562 0.414 0.690 0.530 0.608 0.438 0.622 0.314 0.403 0.661
0.273 0.603 0.599 0.292 0.003 0.688
0.521 0.238 0.554 0.713 0.606 0.429 0.519
0.699 0.730 0.695 0.713 0.982 0.628 0.645 0.663 0.617 0.573 0.818 0.625 0.576 0.670 0.670 0.642 0.695 0.584 0.612 0.670 0.675 0.604 0.628 0.707 0.606 0.579 0.550 0.571 0.584 0.615 0.650 0.651 0.694 0.651 0.073
0.476
0.503
0.239
0.607
0.038
Basic Corruption InfoSocial Basic Human Basic HDIControl Empower- Fabric HDI Security SFI Perception ment Index Index 0.131 0.212 0.465 0.590 0.527 0.125 0.212 0.493 0.464 0.832 0.648 0.368 0.175 0.381 0.458 0.552 0.505 0.094 0.117 0.407 0.454 0.390 0.422 -0.064 0.019 0.282 0.454 0.323 0.389 -0.131 0.194 0.331 0.453 0.824 0.638 0.371 0.049 0.309 0.446 0.523 0.485 0.077 0.335 0.255 0.445 0.779 0.612 0.334 0.024 0.455 0.442 0.509 0.476 0.067 0.141 0.210 0.439 0.731 0.585 0.292 0.015 0.277 0.438 0.729 0.583 0.291 0.189 0.370 0.437 0.710 0.573 0.273 0.087 0.411 0.436 0.525 0.480 0.089 0.146 0.471 0.429 0.494 0.462 0.065 0.039 0.270 0.423 0.504 0.464 0.081 0.058 0.347 0.421 0.511 0.466 0.090 0.218 0.458 0.421 0.449 0.435 0.028 0.222 0.230 0.420 0.396 0.408 -0.024 0.204 0.409 0.408 0.361 0.385 -0.047 0.155 0.382 0.402 0.398 0.400 -0.004 0.170 0.359 0.401 0.407 0.404 0.006 0.180 0.319 0.396 0.572 0.484 0.176 0.029 0.295 0.388 0.427 0.407 0.039 0.252 0.279 0.383 0.517 0.450 0.134 0.330 0.280 0.381 0.551 0.466 0.170 0.165 0.398 0.381 0.320 0.350 -0.061 0.320 0.259 0.376 0.753 0.565 0.377 0.650 0.102 0.376 0.827 0.601 0.451 0.097 0.293 0.370 0.546 0.458 0.176 0.078 0.354 0.362 0.402 0.382 0.040 0.092 0.023 0.361 0.563 0.462 0.202 0.112 0.317 0.360 0.395 0.377 0.035 0.068 0.346 0.355 0.318 0.337 -0.037 0.010 0.094 0.351 0.648 0.500 0.297 0.034 0.271 0.346 0.645 0.496 0.299 0.500 0.149 0.324 0.755 0.540 0.431 0.481 0.188 0.315 0.778 0.547 0.463 0.199 0.391 0.295 0.567 0.431 0.272 0.063 0.068 0.277 0.769 0.523 0.492 0.044 0.411 0.227 0.869 0.548 0.642 0.005 0.012 0.216 0.733 0.474 0.517 0.383 0.044 0.214 0.738 0.476 0.524 0.107 0.266 0.187 0.730 0.458 0.543 0.010 0.202 0.083 0.364 0.224 0.281
Table 2B. Global Societies, sorted by Basic Human Security Index Column 1
C.2
C.3
C.4
C.5
C.6
C.7
C.8
C.9 C.10 Basic Corruption InfoSocial Gender EnvironBasic Human Basic HDIEconomy Peace Control Empower- Fabric Equality ment HDI Security Basic HSI Perception ment Index Index Societies with relatively high Basic Human Security Norway 0.962 0.917 0.851 0.786 0.890 0.881 0.982 0.932 0.050 Iceland 0.903 0.975 0.858 0.696 0.887 0.864 0.971 0.917 0.054 Bermuda 0.854 0.829 0.842 0.981 0.911 0.070 Sweden 0.986 0.881 0.904 0.600 0.903 0.855 0.967 0.911 0.056 Finland 0.957 0.899 0.873 0.726 0.849 0.861 0.955 0.908 0.047 Andorra 0.854 0.769 0.812 0.984 0.898 0.086 Denmark 0.816 0.916 0.772 0.747 0.860 0.822 0.955 0.889 0.066 Netherlands 0.778 0.818 0.717 0.792 0.905 0.802 0.963 0.883 0.080 Switzerland 0.654 0.874 0.845 0.591 0.860 0.765 0.977 0.871 0.106 New Zealand 0.851 0.836 0.747 0.687 0.861 0.797 0.945 0.871 0.074 Germany 0.843 0.873 0.756 0.624 0.835 0.786 0.955 0.871 0.084 Saint Kitts & Nevis 0.945 0.816 0.881 0.853 0.867 -0.014 United Kingdom 0.795 0.763 0.753 0.674 0.851 0.767 0.956 0.862 0.094 Austria 0.692 0.870 0.827 0.572 0.806 0.753 0.962 0.858 0.104 Luxembourg 0.619 0.814 0.618 0.572 0.913 0.707 0.997 0.852 0.145 Hong Kong, China 0.806 0.591 0.808 0.735 0.963 0.849 0.114 Barbados 0.891 0.850 0.642 0.794 0.900 0.847 0.053 Ireland 0.800 0.898 0.737 0.426 0.803 0.733 0.961 0.847 0.114 Canada 0.730 0.860 0.713 0.429 0.844 0.715 0.963 0.839 0.124 Cook Islands 0.964 0.723 0.843 0.829 0.836 -0.007 Belgium 0.730 0.857 0.657 0.541 0.802 0.717 0.955 0.836 0.119 Japan 0.530 0.920 0.776 0.462 0.779 0.693 0.973 0.833 0.140 Australia 0.730 0.807 0.652 0.503 0.784 0.695 0.968 0.832 0.136 Bahamas 0.873 0.908 0.564 0.781 0.881 0.831 0.050 Macau, China 0.694 0.753 0.723 0.932 0.828 0.105 Greece 0.581 0.777 0.731 0.689 0.759 0.707 0.941 0.824 0.117 Spain 0.795 0.783 0.739 0.397 0.757 0.694 0.950 0.822 0.128 France 0.627 0.817 0.786 0.399 0.764 0.679 0.961 0.820 0.141 Singapore 0.570 0.704 0.745 0.726 0.651 0.679 0.959 0.819 0.140 Liechtenstein 0.854 0.435 0.645 0.983 0.814 0.169 Cayman Islands 0.854 0.425 0.639 0.983 0.811 0.172 Portugal 0.665 0.872 0.796 0.420 0.792 0.709 0.907 0.808 0.099 Saint Lucia 0.945 0.830 0.579 0.785 0.828 0.806 0.022 Italy 0.541 0.817 0.748 0.344 0.827 0.655 0.955 0.805 0.150 Slovenia 0.632 0.884 0.769 0.273 0.808 0.673 0.933 0.803 0.130 Societies with mid-range Basic Human Security Cyprus 0.546 0.798 0.828 0.367 0.746 0.657 0.935 0.796 0.139 Taiwan, Prov. Of China 0.702 0.634 0.446 0.828 0.653 0.931 0.792 0.139 Antigua & Barbuda 0.545 0.854 0.809 0.736 0.835 0.786 0.049 Saint Vincent & the Grenadines 0.964 0.816 0.500 0.760 0.811 0.785 0.026 Grenada 0.945 0.718 0.591 0.752 0.811 0.781 0.030 Czech Republic 0.600 0.813 0.708 0.309 0.786 0.643 0.919 0.781 0.138 Korea, Rep. of 0.516 0.812 0.674 0.310 0.807 0.624 0.929 0.776 0.153 Estonia 0.678 0.680 0.662 0.409 0.874 0.660 0.885 0.773 0.112 Kuwait 0.516 0.777 0.357 0.738 0.647 0.607 0.937 0.772 0.165 Seychelles 0.873 0.607 0.593 0.691 0.848 0.769 0.079 Netherlands Antilles 0.854 0.450 0.652 0.886 0.769 0.117 Chile 0.535 0.725 0.795 0.535 0.661 0.650 0.888 0.769 0.119 Croatia 0.732 0.767 0.810 0.185 0.743 0.648 0.889 0.768 0.121 Uruguay 0.570 0.733 0.919 0.389 0.670 0.656 0.871 0.764 0.107 Slovakia 0.622 0.803 0.759 0.179 0.772 0.627 0.900 0.764 0.136
Economy Latvia Lithuania Hungary Dominica Brunei Darussalam USA Virgin Islands Costa Rica Malta Israel United Arab Emirates Guam Qatar Puerto Rico United States Argentina Poland Bulgaria Cape Verde Romania Bahrain Oman Malaysia Macedonia, TFYR Bosnia & Herzegovina Albania Montenegro Fiji Ecuador Jordan Mauritius Samoa Trinidad & Tobago Panama Cuba Colombia Jamaica Belarus Brazil Maldives Venezuela Serbia Saudi Arabia Mexico Moldova Ukraine El Salvador Paraguay Armenia Peru Kazakhstan Dominican Republic Tunisia Lebanon China
Gender EnvironPeace Equality ment 0.765 0.675 0.738 0.728 0.603 0.805
0.829 0.818 0.765 0.945 0.527
0.678 0.756 0.573 0.668 0.400 0.454 0.639
0.851 0.891 0.701 0.438
0.416 0.852
0.005
0.676 0.670 0.611 0.700
0.626 0.809 0.697 0.730 0.982 0.664 0.564 0.691 0.664 0.723 0.762 0.815
0.262 0.711 0.741 0.743
0.638 0.386 0.378 0.524 0.668
0.809 0.747 0.857 0.748 0.724 0.776 0.592 0.700 0.643 0.668 0.459 0.733 0.538 0.638 0.662 0.722 0.700 0.657 0.705 0.576 0.500 0.622 0.311 0.524 0.722 0.619 0.635 0.584 0.581 0.573 0.670 0.597 0.481
0.546 0.554 0.544 0.540 0.650 0.489 0.625 0.648 0.711 0.617 0.615 0.620 0.566 0.644 0.730
0.677 0.581 0.673 0.683 0.475 0.578 0.752
0.945 0.775 0.717 0.945 0.964 0.545 0.748 0.792 0.833 0.711 0.732 0.801 0.964 0.678 0.564 0.699 0.741 0.676 0.711 0.755 0.711 0.768 0.657 0.718 0.728 0.721 0.639
Basic Corruption InfoSocial Basic Human Basic HDIControl Empower- Fabric HDI Security Basic HSI Perception ment Index Index 0.207 0.771 0.649 0.876 0.763 0.113 0.163 0.778 0.645 0.878 0.762 0.116 0.225 0.766 0.633 0.888 0.760 0.128 0.733 0.406 0.695 0.822 0.758 0.064 0.636 0.564 0.576 0.939 0.757 0.182 0.757 0.474 0.615 0.894 0.755 0.139 0.243 0.638 0.633 0.871 0.752 0.119 0.384 0.545 0.598 0.905 0.752 0.153 0.367 0.692 0.565 0.937 0.751 0.186 0.682 0.799 0.602 0.900 0.751 0.149 0.757 0.442 0.599 0.901 0.750 0.151 0.786 0.723 0.557 0.939 0.748 0.191 0.743 0.403 0.573 0.905 0.739 0.166 0.308 0.742 0.523 0.953 0.738 0.215 0.162 0.608 0.592 0.879 0.736 0.143 0.144 0.694 0.577 0.892 0.735 0.157 0.184 0.695 0.610 0.855 0.733 0.122 0.728 0.509 0.740 0.725 0.732 -0.007 0.195 0.757 0.612 0.850 0.731 0.119 0.523 0.636 0.571 0.890 0.731 0.159 0.748 0.415 0.618 0.843 0.730 0.113 0.471 0.605 0.602 0.839 0.721 0.118 0.205 0.656 0.595 0.839 0.717 0.122 0.149 0.652 0.585 0.839 0.712 0.127 0.282 0.531 0.584 0.839 0.711 0.128 0.398 0.742 0.570 0.832 0.701 0.131 0.456 0.482 0.628 0.774 0.701 0.073 0.248 0.560 0.579 0.816 0.697 0.119 0.594 0.489 0.599 0.795 0.697 0.098 0.168 0.659 0.578 0.816 0.697 0.119 0.631 0.164 0.586 0.803 0.695 0.108 0.199 0.684 0.522 0.857 0.690 0.167 0.124 0.569 0.532 0.841 0.686 0.155 0.510 0.125 0.538 0.833 0.686 0.147 0.222 0.496 0.558 0.812 0.685 0.127 0.204 0.734 0.591 0.779 0.685 0.094 0.214 0.521 0.532 0.832 0.682 0.150 0.199 0.560 0.552 0.811 0.682 0.129 0.393 0.474 0.583 0.779 0.681 0.098 0.126 0.522 0.519 0.842 0.681 0.161 0.215 0.592 0.506 0.854 0.680 0.174 0.617 0.467 0.515 0.837 0.676 0.161 0.222 0.424 0.497 0.854 0.675 0.179 0.301 0.553 0.587 0.758 0.673 0.085 0.225 0.622 0.542 0.803 0.672 0.131 0.339 0.569 0.580 0.755 0.667 0.088 0.136 0.496 0.540 0.794 0.667 0.127 0.350 0.482 0.531 0.803 0.667 0.136 0.199 0.487 0.541 0.793 0.667 0.126 0.184 0.478 0.514 0.816 0.665 0.151 0.220 0.527 0.547 0.778 0.663 0.115 0.488 0.403 0.557 0.764 0.660 0.104 0.359 0.503 0.514 0.806 0.660 0.146 0.379 0.240 0.518 0.797 0.657 0.140
Economy Turkey Thailand Bhutan Philippines Sri Lanka Tonga Russian Federation Georgia VietNam Indonesia Libya Azerbaijan Namibia Kiribati Gabon Botswana Suriname Mongolia Algeria South Africa Syria Bolivia Nicaragua Iran, Islamic Republic Egypt Vanuatu Honduras Guatemala American Samoa Ghana Kyrgyzstan Lesotho Uzbekistan Equatorial Guinea Sao Tome & Principe Comoros Tajikistan Morocco Solomon Islands Guyana Swaziland Congo, Republic of the India New Caledonia Senegal Belize Tanzania Micronesia, Federated States of Cambodia Djibouti Madagascar Kenya Lao Peoples Demo. Rep. Uganda
Gender EnvironPeace Equality ment 0.343 0.620 0.627 0.541 0.799 0.846 0.626 0.738 0.583 0.641 0.243 0.586 0.646 0.816 0.554 0.778 0.658 0.616 0.619 0.678 0.669 0.622 0.619 0.603 0.424 0.727 0.465 0.559 0.530 0.378 0.354
0.733 0.644 0.597 0.632 0.478 0.759 0.724 0.762 0.594 0.760
0.584 0.592 0.443 0.695 0.603 0.842 0.581 0.697 0.654 0.615 0.642
0.733 0.736 0.816 0.724 0.743 0.964 0.730 0.746 0.673 0.670 0.628 0.643 0.723 0.982 0.802 0.706 0.597 0.718 0.647 0.663 0.724 0.715 0.663 0.683 0.945 0.748 0.713
0.697 0.389 0.703
0.760 0.726 0.964 0.573 0.818 0.982 0.982 0.625 0.721 0.982 0.550 0.955 0.688 0.658
0.770 0.519 0.668 0.743
0.799 0.073 0.730
0.500 0.728
0.638 0.964 0.670 0.693 0.699 0.742
0.562 0.319 0.683
0.530 0.779 0.543 0.612 0.818 0.630 0.648
Basic Corruption InfoSocial Basic Human Basic HDIControl Empower- Fabric HDI Security Basic HSI Perception ment Index Index 0.195 0.549 0.488 0.820 0.654 0.166 0.116 0.434 0.491 0.815 0.653 0.162 0.806 0.376 0.699 0.605 0.652 -0.047 0.074 0.401 0.534 0.768 0.651 0.117 0.239 0.286 0.518 0.781 0.649 0.132 0.053 0.439 0.485 0.813 0.649 0.164 0.212 0.493 0.464 0.832 0.648 0.184 0.140 0.494 0.491 0.803 0.647 0.156 0.291 0.281 0.542 0.752 0.647 0.105 0.233 0.454 0.538 0.746 0.642 0.104 0.194 0.331 0.453 0.824 0.638 0.186 0.150 0.372 0.480 0.794 0.637 0.157 0.425 0.536 0.606 0.664 0.635 0.029 0.592 0.017 0.530 0.739 0.635 0.104 0.209 0.503 0.562 0.704 0.633 0.071 0.473 0.512 0.591 0.673 0.632 0.041 0.524 0.270 0.471 0.793 0.632 0.161 0.374 0.472 0.528 0.733 0.631 0.102 0.393 0.463 0.526 0.730 0.628 0.102 0.465 0.585 0.581 0.673 0.627 0.046 0.296 0.320 0.501 0.749 0.625 0.124 0.145 0.484 0.527 0.717 0.622 0.095 0.184 0.498 0.538 0.697 0.617 0.080 0.335 0.255 0.445 0.779 0.612 0.167 0.404 0.345 0.509 0.714 0.612 0.102 0.626 0.077 0.549 0.672 0.611 0.061 0.112 0.451 0.497 0.719 0.608 0.111 0.267 0.490 0.522 0.692 0.607 0.085 0.650 0.102 0.376 0.827 0.601 0.226 0.472 0.511 0.637 0.557 0.597 -0.040 0.102 0.451 0.465 0.724 0.595 0.129 0.578 0.413 0.663 0.526 0.594 -0.068 0.141 0.210 0.439 0.731 0.585 0.146 0.015 0.277 0.438 0.729 0.583 0.146 0.388 0.133 0.501 0.662 0.581 0.081 0.345 0.394 0.573 0.575 0.574 0.001 0.189 0.370 0.437 0.710 0.573 0.137 0.286 0.504 0.503 0.642 0.572 0.070 0.490 0.018 0.496 0.647 0.572 0.075 0.320 0.259 0.376 0.753 0.565 0.188 0.403 0.320 0.559 0.550 0.555 -0.005 0.121 0.442 0.487 0.620 0.554 0.066 0.298 0.379 0.486 0.618 0.552 0.066 0.044 0.411 0.227 0.869 0.548 0.321 0.417 0.459 0.611 0.482 0.547 -0.065 0.481 0.188 0.315 0.778 0.547 0.231 0.263 0.450 0.571 0.520 0.545 -0.025 0.500 0.149 0.324 0.755 0.540 0.215 0.073 0.414 0.470 0.605 0.538 0.067 0.286 0.294 0.514 0.553 0.534 0.019 0.158 0.429 0.513 0.551 0.532 0.019 0.160 0.460 0.494 0.567 0.530 0.037 0.131 0.212 0.465 0.590 0.527 0.063 0.262 0.412 0.539 0.508 0.523 -0.015
Economy Turkmenistan Rwanda Cameroon Eritrea Mauritania Myanmar Iraq Zambia Malawi Bangladesh Pakistan Angola Marshall Islands Haiti Korea, Dem. Peop. Rep. of Gambia Yemen Nigeria Zimbabwe Cote d'Ivoire Papua New Guinea Togo Palestinian Authority Sudan Mozambique Nepal Burkina Faso Benin Timor-Leste Mali Central African Republic Ethiopia Congo, Democratic Rep. of the Guinea Guinea Bissau Afghanistan Liberia Chad Burundi Nigeria Sierra Leone Somalia
Basic Corruption InfoSocial Basic Human Basic HDIControl Empower- Fabric HDI Security Basic HSI Perception ment Index Index 0.476 0.503 0.063 0.068 0.277 0.769 0.523 0.246 0.757 0.697 0.558 0.255 0.567 0.478 0.522 -0.044 0.384 0.655 0.695 0.175 0.381 0.458 0.552 0.505 0.047 0.982 0.544 0.010 0.512 0.496 0.504 -0.008 0.411 0.697 0.546 0.325 0.488 0.493 0.512 0.503 0.009 0.606 0.694 0.010 0.094 0.351 0.648 0.500 0.148 Societies with relatively low Basic Human Security 0.429 0.651 0.034 0.271 0.346 0.645 0.496 0.149 0.484 0.756 0.607 0.228 0.446 0.504 0.482 0.493 -0.011 0.535 0.758 0.716 0.257 0.397 0.533 0.449 0.491 -0.042 0.489 0.739 0.645 0.049 0.309 0.446 0.523 0.485 0.038 0.273 0.603 0.604 0.180 0.319 0.396 0.572 0.484 0.088 0.414 0.690 0.576 0.087 0.411 0.436 0.525 0.480 0.045 0.383 0.044 0.214 0.738 0.476 0.262 0.675 0.617 0.024 0.455 0.442 0.509 0.476 0.033 0.239 0.607 0.005 0.012 0.216 0.733 0.474 0.259 0.519 0.711 0.306 0.369 0.476 0.463 0.470 -0.007 0.003 0.688 0.606 0.330 0.280 0.381 0.551 0.466 0.085 0.438 0.622 0.642 0.058 0.347 0.421 0.511 0.466 0.045 0.530 0.608 0.670 0.039 0.270 0.423 0.504 0.464 0.040 0.669 0.681 0.083 0.438 0.468 0.456 0.462 -0.006 0.713 0.615 0.092 0.023 0.361 0.563 0.462 0.101 0.670 0.146 0.471 0.429 0.494 0.462 0.032 0.107 0.266 0.187 0.730 0.458 0.272 0.521 0.571 0.097 0.293 0.370 0.546 0.458 0.088 0.643 0.804 0.653 0.171 0.422 0.539 0.374 0.456 -0.082 0.292 0.707 0.252 0.279 0.383 0.517 0.450 0.067 0.381 0.779 0.643 0.413 0.422 0.528 0.368 0.448 -0.080 0.314 0.695 0.218 0.458 0.421 0.449 0.435 0.014 0.199 0.391 0.295 0.567 0.431 0.136 0.411 0.725 0.644 0.246 0.456 0.496 0.357 0.427 -0.070 0.580 0.713 0.117 0.407 0.454 0.390 0.422 -0.032 0.403 0.661 0.584 0.222 0.230 0.420 0.396 0.408 -0.012 0.599 0.628 0.029 0.295 0.388 0.427 0.407 0.020 0.675 0.170 0.359 0.401 0.407 0.404 0.003 0.670 0.155 0.382 0.402 0.398 0.400 -0.002 0.534 0.982 0.019 0.282 0.454 0.323 0.389 -0.066 0.612 0.204 0.409 0.408 0.361 0.385 -0.024 0.238 0.554 0.584 0.078 0.354 0.362 0.402 0.382 0.020 0.650 0.112 0.317 0.360 0.395 0.377 0.018 0.579 0.165 0.398 0.381 0.320 0.350 -0.030 0.651 0.068 0.346 0.355 0.318 0.337 -0.019 0.038 0.010 0.202 0.083 0.364 0.224 0.140
Gender EnvironPeace Equality ment
Table 3A. Enhanced Human Development Index, its components, and comparison of Enhanced HDI with Basic HDI
Economy Iceland Finland Austria Denmark Norway Netherlands Switzerland Sweden Germany Belgium France Japan Slovakia Slovenia Taiwan, Prov. Of China Canada Czech Republic Singapore Australia Spain Israel Italy New Zealand Ireland Hungary Hong Kong, China United Kingdom Estonia Korea, Rep. of Lithuania Poland Portugal Greece Latvia Bulgaria United States Trinidad & Tobago Romania Costa Rica Malaysia Mauritius Uruguay Jamaica Jordan Russian Federation China Ukraine VietNam Argentina
Corruption InfoSocial EquitaControl Empower- Fabric bility Perception ment Index Index Relatively high “enhanced” (inclusive) Human Development 0.903 0.975 0.858 0.696 0.887 0.864 0.980 0.957 0.899 0.873 0.726 0.849 0.861 0.906 0.692 0.870 0.827 0.572 0.806 0.753 0.893 0.816 0.916 0.772 0.747 0.860 0.822 0.888 0.962 0.917 0.851 0.786 0.890 0.881 0.860 0.778 0.818 0.717 0.792 0.905 0.802 0.858 0.654 0.874 0.845 0.591 0.860 0.765 0.834 0.986 0.881 0.904 0.600 0.903 0.855 0.829 0.843 0.873 0.756 0.624 0.835 0.786 0.835 0.730 0.857 0.657 0.541 0.802 0.717 0.830 0.627 0.817 0.786 0.399 0.764 0.679 0.816 0.530 0.920 0.776 0.462 0.779 0.693 0.790 0.622 0.803 0.759 0.179 0.772 0.627 0.847 0.632 0.884 0.769 0.273 0.808 0.673 0.796 0.702 0.634 0.446 0.828 0.653 0.791 0.730 0.860 0.713 0.429 0.844 0.715 0.752 0.600 0.813 0.708 0.309 0.786 0.643 0.772 0.570 0.704 0.745 0.726 0.651 0.679 0.705 0.730 0.807 0.652 0.503 0.784 0.695 0.684 0.795 0.783 0.739 0.397 0.757 0.694 0.666 Mid-level “enhanced” (inclusive) Human Development 0.668 0.400 0.701 0.367 0.692 0.565 0.659 0.541 0.817 0.748 0.344 0.827 0.655 0.629 0.851 0.836 0.747 0.687 0.861 0.797 0.635 0.800 0.898 0.737 0.426 0.803 0.733 0.595 0.603 0.805 0.765 0.225 0.766 0.633 0.644 0.806 0.591 0.808 0.735 0.568 0.795 0.763 0.753 0.674 0.851 0.767 0.574 0.678 0.680 0.662 0.409 0.874 0.660 0.633 0.516 0.812 0.674 0.310 0.807 0.624 0.586 0.738 0.728 0.818 0.163 0.778 0.645 0.585 0.611 0.741 0.697 0.144 0.694 0.577 0.562 0.665 0.872 0.796 0.420 0.792 0.709 0.533 0.581 0.777 0.731 0.689 0.759 0.707 0.498 0.765 0.675 0.829 0.207 0.771 0.649 0.548 0.700 0.743 0.730 0.184 0.695 0.610 0.564 0.676 0.262 0.626 0.308 0.742 0.523 0.450 0.638 0.546 0.545 0.199 0.684 0.522 0.546 0.638 0.809 0.664 0.195 0.757 0.612 0.527 0.678 0.756 0.851 0.243 0.638 0.633 0.460 0.524 0.748 0.664 0.471 0.605 0.602 0.491 0.538 0.945 0.168 0.659 0.578 0.510 0.570 0.733 0.919 0.389 0.670 0.656 0.453 0.657 0.650 0.711 0.204 0.734 0.591 0.528 0.459 0.733 0.717 0.594 0.489 0.599 0.487 0.641 0.243 0.730 0.212 0.493 0.464 0.443 0.578 0.752 0.639 0.379 0.240 0.518 0.471 0.619 0.566 0.676 0.225 0.622 0.542 0.438 0.646 0.816 0.673 0.291 0.281 0.542 0.466 0.670 0.711 0.809 0.162 0.608 0.592 0.332
Gender Environ Peace Equality -ment
Basic Enhanced HDI HDI
Enhanced HDI Basic HDI
0.971 0.955 0.962 0.955 0.982 0.963 0.977 0.967 0.955 0.955 0.961 0.973 0.900 0.933 0.931 0.963 0.919 0.959 0.968 0.950
0.976 0.931 0.928 0.922 0.921 0.911 0.906 0.898 0.895 0.893 0.889 0.882 0.874 0.865 0.861 0.858 0.846 0.832 0.826 0.808
0.005 -0.025 -0.035 -0.034 -0.061 -0.053 -0.072 -0.069 -0.060 -0.063 -0.073 -0.092 -0.027 -0.069 -0.070 -0.106 -0.074 -0.127 -0.142 -0.142
0.937 0.955 0.945 0.961 0.888 0.963 0.956 0.885 0.929 0.878 0.892 0.907 0.941 0.876 0.855 0.953 0.857 0.850 0.871 0.839 0.816 0.871 0.779 0.795 0.832 0.797 0.803 0.752 0.879
0.798 0.792 0.790 0.778 0.766 0.766 0.765 0.759 0.758 0.732 0.727 0.720 0.720 0.712 0.710 0.702 0.702 0.689 0.666 0.665 0.663 0.662 0.654 0.641 0.638 0.634 0.621 0.609 0.606
-0.139 -0.163 -0.155 -0.183 -0.122 -0.198 -0.191 -0.126 -0.172 -0.147 -0.165 -0.187 -0.222 -0.164 -0.146 -0.252 -0.156 -0.162 -0.206 -0.174 -0.153 -0.209 -0.126 -0.154 -0.195 -0.163 -0.183 -0.143 -0.274
Turkey Thailand Sri Lanka Indonesia Ecuador Egypt Chile Mexico Venezuela Panama Philippines Paraguay Colombia Dominican Republic Peru India Brazil El Salvador Bolivia Honduras Nicaragua Guatemala Bangladesh South Africa Nigeria Zimbabwe
0.343 0.620 0.733 0.195 0.549 0.488 0.386 0.627 0.541 0.736 0.116 0.434 0.491 0.381 0.738 0.583 0.743 0.239 0.286 0.518 0.410 0.554 0.778 0.670 0.233 0.454 0.538 0.422 0.643 0.668 0.775 0.248 0.560 0.579 0.348 0.354 0.760 0.683 0.404 0.345 0.509 0.447 0.535 0.725 0.795 0.535 0.661 0.650 0.268 0.524 0.615 0.699 0.222 0.424 0.497 0.294 0.622 0.648 0.678 0.126 0.522 0.519 0.294 0.662 0.554 0.748 0.124 0.569 0.532 0.286 0.846 0.626 0.724 0.074 0.401 0.534 0.331 0.584 0.730 0.755 0.136 0.496 0.540 0.304 0.700 0.540 0.833 0.222 0.496 0.558 0.283 0.597 0.673 0.718 0.220 0.527 0.547 0.298 0.573 0.677 0.768 0.199 0.487 0.541 0.270 0.389 0.703 0.658 0.298 0.379 0.486 0.431 0.576 0.625 0.801 0.199 0.560 0.552 0.237 0.635 0.644 0.711 0.339 0.569 0.580 0.286 0.559 0.724 0.724 0.145 0.484 0.527 0.293 0.584 0.592 0.748 0.112 0.451 0.497 0.285 Relatively low “enhanced� (inclusive) Human Development 0.530 0.762 0.715 0.184 0.498 0.538 0.300 0.443 0.695 0.713 0.267 0.490 0.522 0.250 0.489 0.739 0.645 0.049 0.309 0.446 0.398 0.727 0.478 0.647 0.465 0.585 0.581 0.236 0.438 0.622 0.642 0.058 0.347 0.421 0.319 0.530 0.608 0.670 0.039 0.270 0.423 0.198
0.820 0.815 0.781 0.746 0.816 0.714 0.888 0.854 0.842 0.841 0.768 0.794 0.812 0.778 0.793 0.618 0.811 0.755 0.717 0.719
0.603 0.598 0.596 0.584 0.582 0.581 0.578 0.574 0.568 0.564 0.550 0.549 0.548 0.538 0.532 0.525 0.524 0.521 0.505 0.502
-0.217 -0.217 -0.186 -0.162 -0.234 -0.134 -0.310 -0.280 -0.274 -0.278 -0.219 -0.245 -0.265 -0.240 -0.262 -0.094 -0.287 -0.235 -0.212 -0.217
0.697 0.692 0.523 0.673 0.511 0.504
0.499 0.471 0.461 0.455 0.415 0.351
-0.199 -0.221 -0.063 -0.219 -0.096 -0.153
Table 3B. Enhanced Human Security Index, its components, and comparisons with Enhanced and Basic Human Development Index Enhanced EnhanCorruption InfoSocial EquitaBasic Basic Human ced Economy Control Empower- Fabric bility HDIHDI Security HDIPerception ment Index Index SFI Index SFI Relatively high “enhanced” human security (e.g. Enhanced/Inclusive human development and security) Iceland 0.903 0.975 0.858 0.696 0.887 0.864 0.980 0.971 0.938 0.075 0.107 Norway 0.962 0.917 0.851 0.786 0.890 0.881 0.860 0.982 0.908 0.027 0.101 Finland 0.957 0.899 0.873 0.726 0.849 0.861 0.906 0.955 0.907 0.047 0.094 Denmark 0.816 0.916 0.772 0.747 0.860 0.822 0.888 0.955 0.888 0.066 0.133 Sweden 0.986 0.881 0.904 0.600 0.903 0.855 0.829 0.967 0.884 0.029 0.112 Netherlands 0.778 0.818 0.717 0.792 0.905 0.802 0.858 0.963 0.874 0.072 0.161 Austria 0.692 0.870 0.827 0.572 0.806 0.753 0.893 0.962 0.869 0.116 0.209 Germany 0.843 0.873 0.756 0.624 0.835 0.786 0.835 0.955 0.859 0.073 0.169 Switzerland 0.654 0.874 0.845 0.591 0.860 0.765 0.834 0.977 0.859 0.094 0.212 Belgium 0.730 0.857 0.657 0.541 0.802 0.717 0.830 0.955 0.834 0.117 0.238 Japan 0.530 0.920 0.776 0.462 0.779 0.693 0.790 0.973 0.819 0.125 0.280 France 0.627 0.817 0.786 0.399 0.764 0.679 0.816 0.961 0.819 0.140 0.282 Canada 0.730 0.860 0.713 0.429 0.844 0.715 0.752 0.963 0.810 0.095 0.248 Slovenia 0.632 0.884 0.769 0.273 0.808 0.673 0.796 0.933 0.801 0.128 0.260 Mid-level “enhanced” human security (e.g. Enhanced/Inclusive human development and security) New Zealand 0.851 0.836 0.747 0.687 0.861 0.797 0.635 0.945 0.792 -0.004 0.148 Taiwan, Pr. Of China 0.702 0.634 0.446 0.828 0.653 0.791 0.931 0.792 0.139 0.278 Slovakia 0.622 0.803 0.759 0.179 0.772 0.627 0.847 0.900 0.791 0.164 0.273 Australia 0.730 0.807 0.652 0.503 0.784 0.695 0.684 0.968 0.782 0.087 0.273 Singapore 0.570 0.704 0.745 0.726 0.651 0.679 0.705 0.959 0.781 0.102 0.280 Czech Republic 0.600 0.813 0.708 0.309 0.786 0.643 0.772 0.919 0.778 0.135 0.276 Spain 0.795 0.783 0.739 0.397 0.757 0.694 0.666 0.950 0.770 0.076 0.256 United Kingdom 0.795 0.763 0.753 0.674 0.851 0.767 0.574 0.956 0.766 -0.001 0.189 Ireland 0.800 0.898 0.737 0.426 0.803 0.733 0.595 0.961 0.763 0.030 0.228 Hong Kong, China 0.806 0.591 0.808 0.735 0.568 0.963 0.755 0.020 0.228 Italy 0.541 0.817 0.748 0.344 0.827 0.655 0.629 0.955 0.746 0.091 0.300 Estonia 0.678 0.680 0.662 0.409 0.874 0.660 0.633 0.885 0.726 0.066 0.225 Hungary 0.603 0.805 0.765 0.225 0.766 0.633 0.644 0.888 0.722 0.089 0.255 Israel 0.668 0.400 0.701 0.367 0.692 0.565 0.659 0.937 0.720 0.155 0.372 Portugal 0.665 0.872 0.796 0.420 0.792 0.709 0.533 0.907 0.716 0.007 0.198 Greece 0.581 0.777 0.731 0.689 0.759 0.707 0.498 0.941 0.715 0.008 0.234 Korea, Rep. of 0.516 0.812 0.674 0.310 0.807 0.624 0.586 0.929 0.713 0.089 0.305 Lithuania 0.738 0.728 0.818 0.163 0.778 0.645 0.585 0.878 0.703 0.058 0.233 Latvia 0.765 0.675 0.829 0.207 0.771 0.649 0.548 0.876 0.691 0.042 0.227 Poland 0.611 0.741 0.697 0.144 0.694 0.577 0.562 0.892 0.677 0.100 0.315 Bulgaria 0.700 0.743 0.730 0.184 0.695 0.610 0.564 0.855 0.676 0.066 0.245 Romania 0.638 0.809 0.664 0.195 0.757 0.612 0.527 0.850 0.663 0.051 0.238 Uruguay 0.570 0.733 0.919 0.389 0.670 0.656 0.453 0.871 0.660 0.004 0.215 Costa Rica 0.678 0.756 0.851 0.243 0.638 0.633 0.460 0.871 0.655 0.022 0.238 Malaysia 0.524 0.748 0.664 0.471 0.605 0.602 0.491 0.839 0.644 0.042 0.237 United States 0.676 0.262 0.626 0.308 0.742 0.523 0.450 0.953 0.642 0.119 0.430 Trinidad & Tobago 0.638 0.546 0.545 0.199 0.684 0.522 0.546 0.857 0.642 0.119 0.335 Mauritius 0.538 0.945 0.168 0.659 0.578 0.510 0.816 0.635 0.057 0.238 Jamaica 0.657 0.650 0.711 0.204 0.734 0.591 0.528 0.779 0.633 0.042 0.188 Jordan 0.459 0.733 0.717 0.594 0.489 0.599 0.487 0.795 0.627 0.028 0.196 Chile 0.535 0.725 0.795 0.535 0.661 0.650 0.268 0.888 0.602 -0.048 0.238 Argentina 0.670 0.711 0.809 0.162 0.608 0.592 0.332 0.879 0.601 0.009 0.287 China 0.578 0.752 0.639 0.379 0.240 0.518 0.471 0.797 0.595 0.078 0.279 Ukraine 0.619 0.566 0.676 0.225 0.622 0.542 0.438 0.803 0.594 0.053 0.261 VietNam 0.646 0.816 0.673 0.291 0.281 0.542 0.466 0.752 0.587 0.045 0.210 Ecuador 0.643 0.668 0.775 0.248 0.560 0.579 0.348 0.816 0.581 0.002 0.237 Russian Federation 0.641 0.243 0.730 0.212 0.493 0.464 0.443 0.832 0.580 0.116 0.368 Sri Lanka 0.738 0.583 0.743 0.239 0.286 0.518 0.410 0.781 0.570 0.052 0.263 Indonesia 0.554 0.778 0.670 0.233 0.454 0.538 0.422 0.746 0.569 0.031 0.208 Gender EnvironPeace Equality ment
Turkey 0.343 0.620 0.733 0.195 0.549 0.488 0.386 0.820 0.565 0.077 Thailand 0.627 0.541 0.736 0.116 0.434 0.491 0.381 0.815 0.562 0.072 Egypt 0.354 0.760 0.683 0.404 0.345 0.509 0.447 0.714 0.557 0.048 Panama 0.662 0.554 0.748 0.124 0.569 0.532 0.286 0.841 0.553 0.021 Venezuela 0.622 0.648 0.678 0.126 0.522 0.519 0.294 0.842 0.552 0.032 Colombia 0.700 0.540 0.833 0.222 0.496 0.558 0.283 0.812 0.551 -0.007 Mexico 0.524 0.615 0.699 0.222 0.424 0.497 0.294 0.854 0.548 0.051 Paraguay 0.584 0.730 0.755 0.136 0.496 0.540 0.304 0.794 0.546 0.006 Philippines 0.846 0.626 0.724 0.074 0.401 0.534 0.331 0.768 0.544 0.010 Dominican Republic 0.597 0.673 0.718 0.220 0.527 0.547 0.298 0.778 0.541 -0.006 El Salvador 0.635 0.644 0.711 0.339 0.569 0.580 0.286 0.755 0.540 -0.039 Peru 0.573 0.677 0.768 0.199 0.487 0.541 0.270 0.793 0.535 -0.006 Brazil 0.576 0.625 0.801 0.199 0.560 0.552 0.237 0.811 0.533 -0.019 Bolivia 0.559 0.724 0.724 0.145 0.484 0.527 0.293 0.717 0.512 -0.015 Nicaragua 0.530 0.762 0.715 0.184 0.498 0.538 0.300 0.697 0.512 -0.026 India 0.389 0.703 0.658 0.298 0.379 0.486 0.431 0.618 0.512 0.026 Honduras 0.584 0.592 0.748 0.112 0.451 0.497 0.285 0.719 0.500 0.003 Relatively low “enhanced� human security (e.g. Enhanced/Inclusive human development and security) South Africa 0.727 0.478 0.647 0.465 0.585 0.581 0.236 0.673 0.497 -0.084 Guatemala 0.443 0.695 0.713 0.267 0.490 0.522 0.250 0.692 0.488 -0.034 Bangladesh 0.489 0.739 0.645 0.049 0.309 0.446 0.398 0.523 0.456 0.010 Nigeria 0.438 0.622 0.642 0.058 0.347 0.421 0.319 0.511 0.417 -0.004 Zimbabwe 0.530 0.608 0.670 0.039 0.270 0.423 0.198 0.504 0.375 -0.048
0.332 0.324 0.205 0.309 0.323 0.254 0.357 0.254 0.234 0.231 0.175 0.252 0.259 0.190 0.159 0.132 0.222 0.092 0.170 0.077 0.090 0.081